EIPAAB_data_summary
Jake Martin
2024-04-24
1 READ ME
2 Welocme
This is the the R markdown script written in R studio (2023.09.0+463 “Desert Sunflower” Release) used to summarise the systematic map database from Martin et al. 2024 “Evidence of the impacts of pharmaceuticals on aquatic animal behaviour (EIPAAB): a systematic map and open access database” (doi: XXXXX).
It is designed to act as a starting point for anyone who wishes to use the ‘Evidence of the Impacts of Pharmaceuticals on Aquatic Animal Behaviour’ (EIPAAB) database for their own projects.
This script was authored by Jake M Martin (jakemartin.org)
Contact: jake.martin@deakin.edu or jake.martin@slu.se *P.s Apologies for any spelling mistakes in the script I am dyslexic and this is a very long document
3 R informartion
If you are not familiar with R, here’s a beginners guide (https://www.youtube.com/watch?v=_V8eKsto3Ug).
Here’s a link to download R (https://cran.r-project.org/) and R studio (https://posit.co/products/open-source/rstudio/), and a guide on how to do so.
This is an R markdown file, which makes annotating and running R code more user friendly, it is also easy to reproducible and share in a variate of formates (e.g. PDF). The R code is embed within chunks, and the output for code will be embedded under the chuck.
# This is a chuckAll other text outside of the chucks are annotations (like this). Hashtags used outside of chucks are used to create headers and to structure the file. Hashtags within the chucks are used for more precise annotation within the code.
If you are not familiar with R markdown, here’s a guide (https://www.youtube.com/watch?v=tKUufzpoHDE)
4 Directories
Creating out input and output directories. They will be made within the current parent directory (i.e. where the R sciprt is saved)
This is code creates a folder and saves the directory as figure_path. This is where we will export our figures
figures_path <- paste0(getwd(), "/figures")
if (!dir.exists(figures_path)) {
dir.create(figures_path)
}This is code creates a folder and saves the directory as output_path This is where we will export our data
output_path <- paste0(getwd(), "/output-data")
if (!dir.exists(output_path)) {
dir.create(output_path)
}Input directory
input_path <- paste0(getwd(), "/input-data")
if (!dir.exists(figures_path)) {
dir.create(figures_path)
}4 Database informartion
The ‘Evidence of the Impacts of Pharmaceuticals on Aquatic Animal Behaviour’ (EIPAAB) database has 96 columns and 1754 rows. The columns represent various forms of metadata extracted from articles that were included in Martin et al. 2024 “Evidence of the impacts of pharmaceuticals on aquatic animal behaviour: a systematic map and open access database” (doi: XXXXX).
The READ-ME file which explains what each metadata is, how it was extracted, what structure it has, and at what level it applies, is available at XXX. Below I have imported the read me for accessibility. I highly recommend you read the READ-ME before conducting any of your own meta-analysis to make sure you have interoperated the data correctly.
More generally, column names that start with ‘validity’ are metadata relating to study validity, those that start with ‘specie’s relate to species information (population), those that start with ’compound’ relate to the chemical information (exposure), those that start with ‘behav’ relate to behaviour information (outcome). The order of columns reflects both the level the metadata is extracted at (i.e. article level or species by compound level; see level in READ-ME), as well as the general category of metadata (i.e. validity, species, compound, behaviour).
setwd(input_path)
READ_ME <- read.csv("READ-ME.csv", na = "NA") # loading the READ-ME file
READ_ME # Outputting the READ-ME file as a tibble## column_name type
## 1 article_id string
## 2 response_id string
## 3 doi string
## 4 title string
## 5 year interger
## 6 journal string
## 7 authors string
## 8 url_for_pdf link
## 9 screener catgorical
## 10 validity_guideline boolean
## 11 validity_good_laboratory_practice boolean
## 12 validity_survival_growth_reproduction boolean
## 13 validity_animal_feeding boolean
## 14 validity_water_quality boolean
## 15 validity_light_cycle boolean
## 16 validity_randomization boolean
## 17 validity_behav_scoring_method catgorical/string list
## 18 validity_behav_blinding boolean
## 19 validity_conflict_statement catgorical
## 20 species_n interger
## 21 compound_n interger
## 22 compond_mixture boolean
## 23 additional_biomarkers boolean
## 24 study_motivation catgorical
## 25 unique_row_id string
## 26 species_name string
## 27 species_common_name string
## 28 species_iucn_doi link
## 29 species_iucn_status catgorical
## 30 species_iucn_habitat catgorical
## 31 species_source catgorical/string
## 32 species_stage catgorical/string
## 33 species_sex catgorical/string
## 34 species_ncbi_taxonomy_id string
## 35 species_ncbi_lineage string list
## 36 species_domain string
## 37 species_kingdom string
## 38 species_phylum string
## 39 species_class string
## 40 species_order string
## 41 species_family string
## 42 species_genus string
## 43 species_species string
## 44 compound_name string
## 45 compound_cas string
## 46 compound_pubchem_cid interger
## 47 compound_pubchem_name string
## 48 compound_synonyms string list
## 49 compound_atc_classification string list
## 50 compound_atc_boolean boolean
## 51 compound_atc_n interger
## 52 compound_atc_level_1 string list
## 53 compound_atc_level_2 string list
## 54 compound_atc_level_3 string list
## 55 compound_atc_level_4 string list
## 56 compound_atc_level_5 string list
## 57 compound_expose_route catgorical
## 58 compound_treatment_levels interger
## 59 compound_min_dose interger
## 60 compound_min_dose_unit catgorical
## 61 compound_min_dose_std interger
## 62 compound_min_dose_unit_std catgorical
## 63 compound_max_dose interger
## 64 compound_max_dose_unit catgorical
## 65 compound_max_dose_std interger
## 66 compound_max_dose_unit_std catgorical
## 67 compound_min_duration_exposure catgorical
## 68 compound_max_duration_exposure catgorical
## 69 compound_exposure_location catgorical
## 70 validity_compound_cas_reported boolean
## 71 validity_compound_purity_reported boolean
## 72 validity_compound_water_verification catgorical
## 73 validity_compound_animal_verification boolean
## 74 behav_movement_boolean boolean
## 75 behav_movement catgorical/string list
## 76 behav_boldness_boolean boolean
## 77 behav_boldness catgorical/string list
## 78 behav_foraging_boolean boolean
## 79 behav_foraging catgorical/string list
## 80 behav_antipredator_boolean boolean
## 81 behav_antipredator catgorical/string list
## 82 behav_mating_boolean boolean
## 83 behav_mating catgorical/string list
## 84 behav_post_mating_boolean boolean
## 85 behav_post_mating catgorical/string list
## 86 behav_agression_boolean boolean
## 87 behav_agression catgorical/string list
## 88 behav_sociality_boolean boolean
## 89 behav_sociality catgorical/string list
## 90 behav_cognition_boolean boolean
## 91 behav_cognition catgorical/string list
## 92 behav_noncat_boolean boolean
## 93 behav_noncat catgorical/string list
## 94 behav_is_social_context boolean
## 95 behav_test_location catgorical/string list
## 96 comments string
## 97 wos_journal_iso_abbrev string
## 98 wos_author_keywords string list
## 99 wos_id string
## 100 wos_categories catgorical/string list
## 101 wos_research_areas catgorical/string list
## 102 wos_cited_references interger
## 103 wos_times_cited_05_07_2024 interger
## 104 scopus_jounral_abbrev string
## 105 scopus_author_keywords string list
## 106 scopus_id string
## 107 scopus_times_cited_05_07_2024 interger
## structure
## 1 free
## 2 free
## 3 free
## 4 free
## 5 free
## 6 free
## 7 <Last name>, ", ", <First initial>, " " <Middle initial>, " and ", <Last name>, ", ", <First initial>, " " <Middle initial>, " and "…
## 8 https
## 9 "JMM", "EST", "ESM", "LML", "KNF", "NSH", "SO", "IYL", "DC", "MRM", "JAB", "HT", "KRS", "JS", "MGB", "ML"
## 10 "Yes", "No"
## 11 "Yes", "No"
## 12 "Yes", "No"
## 13 "Yes", "No"
## 14 "Yes", "No"
## 15 "Yes", "No"
## 16 "Yes", "No"
## 17 "supervised automated tracking approaches", "manual or human scoring from videos or image", "live scoring in real time", "quantifying food consumption", "sensory for physical movement", "acoustic analysis software", "other", "not specified"
## 18 "Yes", "No"
## 19 "No conflict stated", "No statement is made in the paper", "Some conflict stated"
## 20 free
## 21 free
## 22 "Yes", "No"
## 23 "Yes", "No"
## 24 "Environmental", "Medical", "Basic research"
## 25 response_id + species_name + compound_name
## 26 free
## 27 free
## 28 https
## 29 "Not Evaluated", "Data Deficient", "Least Concern", "Near Threatened", "Vulnerable", "Endangered", "Critically Endangered", "Extinct in the Wild", "Extinct"
## 30 Forest, Savanna, Shrubland, Grassland, Wetlands, Rocky areas (eg. inland cliffs, mountain peaks), Caves and Subterranean Habitats (non-aquatic), Desert, Marine Neritic, Marine Oceanic, Marine Deep Benthic, Marine Intertidal, Marine Coastal/Supratidal, Artificial/Terrestrial, Artificial/Aquatic & Marine, Introduced vegetation, Other, Unknown
## 31 "Commercial supplier or fish farm", "Lab stock of undisclosed origin", "Wild collected", "Lab stock from commercial supplier", "Lab stock from wild population", "Not reported"
## 32 "Egg or embryo", "Larvae", "Juvenile", "Adult", "Unknown or not specified"
## 33 "Male", "Female", "Hermaphrodites", "Unknown or not specified"
## 34 free
## 35 semi colon separated list
## 36 free
## 37 free
## 38 free
## 39 free
## 40 free
## 41 free
## 42 free
## 43 free
## 44 free
## 45 free
## 46 free
## 47 free
## 48 <Name 1>, "; ", <Name 2>, "; "…
## 49 <compound with country code>, "; ", <compound with ID>, "; ", <KEGG ATC Level 1>, "; " <KEGG ATC Level 2>, ";"…
## 50 "Yes", "No"
## 51 free
## 52 semi colon separated list
## 53 semi colon separated list
## 54 semi colon separated list
## 55 semi colon separated list
## 56 semi colon separated list
## 57 "Waterborne only", "Waterborne plus any other route", "Other exposure route"
## 58 free
## 59 free
## 60 "ug/L", "mg/L", "uM", "ng/L", "mM", "M/L", "pg/L", "nM", "mg/mL", "ppm", "M", "ug/mL", "mM/L", "g/L", "uL/mL", "aM" , "fM", "g/mL", "mg/kg", "ng/mL", "ng/uL", "ppb", "ug/kg"
## 61 free
## 62 "ug/L", "uM", "uM/L", "ppm", "uL/L", "ug/g", NA
## 63 free
## 64 "ug/L", "mg/L", "uM", "ng/L", "mM", "M/L", "pg/L", "nM", "mg/mL", "ppm", "M", "ug/mL", "mM/L", "g/L", "uL/mL", "aM" , "fM", "g/mL", "mg/kg", "ng/mL", "ng/uL", "ppb", "ug/kg"
## 65 free
## 66 "ug/L", "uM", "uM/L", "ppm", "uL/L", "ug/g", NA
## 67 "Less than 6 hours", "6 to 24 hours", "1 to 3 days", "3 to 8 days", "8 to 15 days", "15 to 22 days", "22 to 29 days", "1 to 3 months", "3 to 6 months", "Lifetime", "Transgenerational", "Multigenerational", "Not stated"
## 68 "Less than 6 hours", "6 to 24 hours", "1 to 3 days", "3 to 8 days", "8 to 15 days", "15 to 22 days", "22 to 29 days", "1 to 3 months", "3 to 6 months", "Lifetime", "Transgenerational", "Multigenerational", "Not stated"
## 69 "Indoor laboratory setting or assumed indoors", "Outdoor restricted setting (cannot interact with wild species)", "Outdoor natural setting"
## 70 "Yes", "No"
## 71 "Yes", "No"
## 72 "Nominal", "Measured"
## 73 "Yes", "No"
## 74 "Yes", "No"
## 75 "normal locomotor activity", "light stimulated locomotor activity", "abnormal movement", "physical contact stimulated locomotor activity", "migration or dispersal", "auditory stimulated locomotor activity", "light stimulated locomotor", "exercise challenge", NA
## 76 "Yes", "No"
## 77 "novel tank or open-field or exploration assay", "locomotor activity within this context", "dark-light arena", "lighting on-off", "sheltering or hiding", "shoaling tendency", "novel object", "stress related response to simulated attack", "stress related feeding", NA
## 78 "Yes", "No"
## 79 "feeding on a non-motile food source", "feeding on a motile food source", "locomotor activity within this context", "feeding on unspecified food source", "responsiveness to food cue", NA
## 80 "Yes", "No"
## 81 "response to a simulated predator", "locomotor activity within this context", "response to a predation olfactory cue", "response to a live predator behind a barrier", "response to a live predator free to interact", NA
## 82 "Yes", "No"
## 83 "pre copulatory and mating behaviour", "pre copulatory behaviour with physical barrier", "mate choice or preference", "nesting behaviour", "non-interactive pre copulatory behaviour", "locomotor activity within this context", "direct mate competition", NA
## 84 "Yes", "No"
## 85 "offspring care", "resource defence", NA
## 86 "Yes", "No"
## 87 "aggression towards a mirror", "aggression towards a live competitor free to interact", "aggression towards a live competitor with physical barrier", "aggression towards a model or video", "locomotor activity within this context", NA
## 88 "Yes", "No"
## 89 "affiliation with a live conspecific behind a barrier", "affiliation with a live conspecific free to interact", "locomotor activity within this context", "affiliation with a conspecific model or video or mirror", "social choice between some number of options", "unexplained sociality test", "affiliation with a live heterospecific free to interact", "undirected social behaviour", NA
## 90 "Yes", "No"
## 91 "positive reinforcement associative learning", "negative reinforcement associative learning", "memory or information retention", "locomotor activity within this context", "habituation task", "novel task problem solving", NA
## 92 "Yes", "No"
## 93 "attraction or aversion to compound", "circadian activity", "righting behaviour", "pain response", "sedation", "colour preference", "thermal preference behaviour", "exercise challenge", "camouflage", "lateralization", NA
## 94 "Yes", "No"
## 95 "Indoor laboratory setting or assumed indoors", "Outdoor restricted setting (cannot interact with wild species)", "Outdoor natural setting"
## 96 free
## 97 free
## 98 semi colon separated list
## 99 free
## 100 semi colon separated list
## 101 semi colon separated list
## 102 free
## 103 free
## 104 free
## 105 semi colon separated list
## 106 free
## 107 free
## level
## 1 article
## 2 article
## 3 article
## 4 article
## 5 article
## 6 article
## 7 article
## 8 article
## 9 article
## 10 article
## 11 article
## 12 article
## 13 article
## 14 article
## 15 article
## 16 article
## 17 article
## 18 article
## 19 article
## 20 article
## 21 article
## 22 article
## 23 article
## 24 article
## 25 species by compound
## 26 species by compound
## 27 species by compound
## 28 species by compound
## 29 species by compound
## 30 species by compound
## 31 species by compound
## 32 species by compound
## 33 species by compound
## 34 species by compound
## 35 species by compound
## 36 species by compound
## 37 species by compound
## 38 species by compound
## 39 species by compound
## 40 species by compound
## 41 species by compound
## 42 species by compound
## 43 species by compound
## 44 species by compound
## 45 species by compound
## 46 species by compound
## 47 species by compound
## 48 species by compound
## 49 species by compound
## 50 species by compound
## 51 species by compound
## 52 species by compound
## 53 species by compound
## 54 species by compound
## 55 species by compound
## 56 species by compound
## 57 species by compound
## 58 species by compound
## 59 species by compound
## 60 species by compound
## 61 species by compound
## 62 species by compound
## 63 species by compound
## 64 species by compound
## 65 species by compound
## 66 species by compound
## 67 species by compound
## 68 species by compound
## 69 species by compound
## 70 species by compound
## 71 species by compound
## 72 species by compound
## 73 species by compound
## 74 species by compound
## 75 species by compound
## 76 species by compound
## 77 species by compound
## 78 species by compound
## 79 species by compound
## 80 species by compound
## 81 species by compound
## 82 species by compound
## 83 species by compound
## 84 species by compound
## 85 species by compound
## 86 species by compound
## 87 species by compound
## 88 species by compound
## 89 species by compound
## 90 species by compound
## 91 species by compound
## 92 species by compound
## 93 species by compound
## 94 species by compound
## 95 species by compound
## 96 article
## 97 article
## 98 article
## 99 article
## 100 article
## 101 article
## 102 article
## 103 article
## 104 article
## 105 article
## 106 article
## 107 article
## description
## 1 A unique article id assigned during title and abstract screening
## 2 A unique response id generated by the survey used to extract the article
## 3 The DOI of the article
## 4 The title of the atricle
## 5 The year the article was publish
## 6 The name of the journal in which the article was published
## 7 The names of the authors who published the article. For example "Martin, J M and Bertram, M G".
## 8 A link to a freely available copy of the article if it is available. Link located using roadoi (https://cran.r-project.org/web/packages/roadoi/index.html)
## 9 The initials of the screener who extracted the atricle
## 10 A boolean measure of whether some kind of official guideline was followed in the article (e.g. OECD)
## 11 A boolean measure of whether 'good lab practices' were followed
## 12 A boolean measure of whether and measures of surival, growth or reproductive success were scored in addition to behaviour
## 13 A boolean measure of whether animal feeding was described
## 14 A boolean measure of whether any water quality metrics were reported
## 15 A boolean measure of whether light cycles were described
## 16 A boolean measure of whether randomisation was used during treatment allocation
## 17 A broad category for the method used to measure behaviour. Can have multiple categories separated by a comma.
## 18 A boolean measure of whether blinding was used during behavioural scoring
## 19 Whether a conflict statement was made, and if so, was there a conflict state
## 20 The number of unique species in the article
## 21 The number of unique compounds in the article
## 22 A boolean measure of whether mixture exposures were also used
## 23 A boolean measure of whether other any additional sub-organismal endpoints of exposure were measured
## 24 A broad category for overall motivation of the article
## 25 A unique_id for each row of data which represents each articles species by compound combination
## 26 The current scientific (Latin) name of the study species
## 27 The common name of the study species, taken from one of NCBI, IUCN red list, or Wikipedia
## 28 The link to the IUCN red list report for the study species (https://www.iucnredlist.org/)
## 29 The nine IUCN statuses for species (https://www.iucnredlist.org/)
## 30 The IUCN current Habitats Classification Scheme (Version 3.1) (https://www.iucnredlist.org/resources/habitat-classification-scheme)
## 31 The source of the animal used in the behavioural experiment. Can have multiple categories separated by a comma.
## 32 The animals life stage for which behaviour was measured. Can have multiple categories separated by a comma.
## 33 The sex of the animal used in behavioural experiments. Can have multiple categories separated by a comma.
## 34 The unique taxonomy ID that is given to each species in the NCBI taxonomy database (https://www.ncbi.nlm.nih.gov/taxonomy)
## 35 The full linage given in the NCBI taxonomy database (https://www.ncbi.nlm.nih.gov/taxonomy)
## 36 Classification at the Domain level
## 37 Classification at the Kingdom level
## 38 Classification at the Phylum level
## 39 Classification at the Class level
## 40 Classification at the Order level
## 41 Classification at the Family level
## 42 Classification at the Genus level
## 43 Classification at the Species level
## 44 The name of the compound used in the study
## 45 The CAS number provided in PubChem. A CAS Registry Number is a numeric identifier that can contain up to 10 digits, divided by hyphens into three parts. The right-most digit is a check digit used to verify the validity and uniqueness of the entire number. For example, 58-08-2 is the CAS Registry Number for caffeine
## 46 A PubChem Compound Identification, a non-zero integer PubChem accession identifier for a unique chemical structure. For example, the CID of aspirin is 2244 (https://pubchem.ncbi.nlm.nih.gov/search/help_search.html#:~:text=CID%20%2D%2D%20PubChem%20Compound%20Identification,each%20element%20in%20a%20compound.)
## 47 The primary name given to the compound on PubChem
## 48 The synonyms provided by PubChem
## 49 The WHO Collaborating Centre for Drug Statistics Methodology Anatomical Therapeutic Chemical (ATC) classification tree (https://atcddd.fhi.no/atc_ddd_index/)
## 50 Whether there was a ATC for the compound
## 51 The number of different ATC classifications for that compound
## 52 The first classification level for the ATC(s) of the compound
## 53 The second classification level for the ATC(s) of the compound
## 54 The third classification level for the ATC(s) of the compound
## 55 The fourth classification level for the ATC(s) of the compound
## 56 The fith classification level for the ATC(s) of the compound
## 57 The route to which animals were exposed to the compound
## 58 The number of different treatment groups/levels/dose for the compound
## 59 The minimum dose the animal was exposed to before behavioural experiments
## 60 The units used to report the minimum dose in the article
## 61 The standardised value of the minimum dose based on conversion to one of the six standard units used in this database. Convert mass/volume measures to ug/L, convert volume/volume measures to uL/L, convert mass/mass measures to ug/g, convert mole units to uM, convert molarity (mole/volume) units to uM/L, and convert dimensionless units (ppb and ppm) of concentration to ppm.
## 62 The value of the minium dose after conversion to one of the six standard units used it simply cross-comparison in the database
## 63 The maximum dose the animal was exposed to before behavioural experiments
## 64 The units used to report the maximum dose in the article
## 65 The standardised value of the maximum dose based on conversion to one of the six standard units used in this database. Convert mass/volume measures to ug/L, convert volume/volume measures to uL/L, convert mass/mass measures to ug/g, convert mole units to uM, convert molarity (mole/volume) units to uM/L, and convert dimensionless units (ppb and ppm) of concentration to ppm.
## 66 The value of the maximum dose after conversion to one of the six standard units used it simply cross-comparison in the database
## 67 The minimum duration of exposure before any behaviour was measured
## 68 The maximum duration of exposure before any behaviour was measured
## 69 The location of the exposure
## 70 Was the compounds CAS number reported in the article
## 71 Was the compounds purity reported in the article
## 72 Whether the water concentration of the compound were measured or nominal
## 73 A boolean measure of whether internal concentrations of the compound were measured in the animal
## 74 A boolean measure of whether any movement or motion-related behaviours were measured
## 75 Can have multiple categories separated by a comma. See Table SX for a discretion of each category (LINK)
## 76 A boolean measure of whether any boldness or anxiety-related behaviours were measured
## 77 Can have multiple categories separated by a comma. See Table SX for a discretion of each category (LINK)
## 78 A boolean measure of whether any feeding or foraging behaviours were measured
## 79 Can have multiple categories separated by a comma. See Table SX for a discretion of each category (LINK)
## 80 A boolean measure of whether antipredator behaviours were measured
## 81 Can have multiple categories separated by a comma. See Table SX for a discretion of each category (LINK)
## 82 A boolean measure of whether pre-copulatory or actual mating behaviours were measured
## 83 Can have multiple categories separated by a comma. See Table SX for a discretion of each category (LINK)
## 84 A boolean measure of whether any post-copulatory reproductive behaviours were measured
## 85 Can have multiple categories separated by a comma. See Table SX for a discretion of each category (LINK)
## 86 A boolean measure of any aggressive behaviours were measured
## 87 Can have multiple categories separated by a comma. See Table SX for a discretion of each category (LINK)
## 88 A boolean measure of any social or sociability related behaviours were measured
## 89 Can have multiple categories separated by a comma. See Table SX for a discretion of each category (LINK)
## 90 A boolean measure of any cognition related behaviours were measured
## 91 Can have multiple categories separated by a comma. See Table SX for a discretion of each category (LINK)
## 92 A boolean measure of any otherwise uncategorised behaviours were measured
## 93 Can have multiple categories separated by a comma. See Table SX for a discretion of each category (LINK)
## 94 A boolean measure of whether or not behaviour was measured in a social context where conspecifics were free to interact
## 95 The location where behaviour was assessed. Can have multiple categories separated by a comma.
## 96 Comments left by screeners during the extraction
## 97 Web of Science ISO journal name abbreviation
## 98 Web of Science unique article ID
## 99 Web of Science author keywords
## 100 Every journal and book covered by Web of Science core collection is assigned to at least one resarch area. For category list see https://webofscience.help.clarivate.com/en-us/Content/wos-core-collection/wos-core-collection.htm?Highlight=category
## 101 Every journal and book covered by Web of Science core collection is assigned to at least one subject categories. Every record in Web of Science core collection contains the subject category of its source publication in the Web of Science Categories field. For category list see https://webofscience.help.clarivate.com/en-us/Content/wos-core-collection/wos-core-collection.htm?Highlight=category
## 102 The number of references cite within the article according to Web of Science
## 103 The number of times the article has been cited according to Web of Science on the 05/07/2024
## 104 Scopus journal name abbreviation
## 105 Scopus author key words
## 106 Scopus unique article ID
## 107 The number of times the article has been cited according to Scopus on the 05/07/2024
## validity_assessment CRED_criteria
## 1 0 NA
## 2 0 NA
## 3 0 NA
## 4 0 NA
## 5 0 NA
## 6 0 NA
## 7 0 NA
## 8 0 NA
## 9 0 NA
## 10 1 1
## 11 1 2
## 12 1 3
## 13 1 11
## 14 1 11
## 15 1 11
## 16 1 NA
## 17 1 NA
## 18 1 NA
## 19 1 NA
## 20 0 NA
## 21 0 NA
## 22 0 NA
## 23 0 NA
## 24 0 NA
## 25 0 NA
## 26 0 NA
## 27 0 NA
## 28 0 NA
## 29 0 NA
## 30 0 NA
## 31 1 9
## 32 1 8
## 33 1 8
## 34 0 NA
## 35 0 NA
## 36 0 NA
## 37 0 NA
## 38 0 NA
## 39 0 NA
## 40 0 NA
## 41 0 NA
## 42 0 NA
## 43 0 NA
## 44 0 NA
## 45 0 NA
## 46 0 NA
## 47 0 NA
## 48 0 NA
## 49 0 NA
## 50 0 NA
## 51 0 NA
## 52 0 NA
## 53 0 NA
## 54 0 NA
## 55 0 NA
## 56 0 NA
## 57 0 NA
## 58 0 NA
## 59 0 NA
## 60 0 NA
## 61 0 NA
## 62 0 NA
## 63 0 NA
## 64 0 NA
## 65 0 NA
## 66 0 NA
## 67 1 14
## 68 1 14
## 69 0 NA
## 70 1 5
## 71 1 6
## 72 1 15
## 73 1 15
## 74 0 NA
## 75 0 NA
## 76 0 NA
## 77 0 NA
## 78 0 NA
## 79 0 NA
## 80 0 NA
## 81 0 NA
## 82 0 NA
## 83 0 NA
## 84 0 NA
## 85 0 NA
## 86 0 NA
## 87 0 NA
## 88 0 NA
## 89 0 NA
## 90 0 NA
## 91 0 NA
## 92 0 NA
## 93 0 NA
## 94 0 NA
## 95 0 NA
## 96 0 NA
## 97 0 NA
## 98 0 NA
## 99 0 NA
## 100 0 NA
## 101 0 NA
## 102 0 NA
## 103 0 NA
## 104 0 NA
## 105 0 NA
## 106 0 NA
## 107 0 NA5 Required R packages
These are the R packages required to run the script. I have added them to a list so that I can install them all in one go using the function below called loaded_packages. This function I have made will load all the packages in the list below, if the packages are not already installed, this function will first install them.
If you want to install and load each package separately, you can use the code install.packages() and require(), I have given a example below.
required_packages <- c("tidyverse",
"ggraph",
"igraph",
"ggrepel",
"RColorBrewer",
"ggtree",
"treeio",
"ape",
"gridExtra",
"ggdist",
"highcharter"
)
# Alternatively, you can install an load them one by one
# install.packages("tidyverse")
# require("tidyverse")This is the function to load all packages (and install if necessary) in the list above.
type = ‘source’ instructs R to download and install the package from its source code rather than from a precompiled binary, this is optional.
loaded_packages <- lapply(required_packages, function(package) {
if (!require(package, character.only = TRUE)) {
install.packages(package, type = 'source') # type = 'source' is optional
if (!require(package, character.only = TRUE)) {
return(FALSE)
}
}
return(TRUE)
})
# Check if all packages are loaded successfully
if (all(unlist(loaded_packages))) {
cat("All packages loaded\n")
} else {
cat("Some packages failed to load or install\n")
}For ggtree and treeio you may need to run this code for instillation
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("ggtree")8 Importing EIPAAB database
Importing the EIPAAB-database.csv database (accessed from: https://osf.io/atwy6/).
If the CSV files are in the same working directory (wd) as this R script, you will not need to use setwd(), but if the files are located elsewhere you will need to specify this in setwd(), and run all lines at once. In R markdown the working directory changes back to default after the chuck is run.
setwd(input_path)
EIPAAB_database <- read.csv("EIPAAB-database.csv", na = "NA")9 Total numbers
The first thing we will look at is how many unique (distinct) articles there are in the database, and how many rows of data there are.
There are 901 articles, with 1740 rows.
EIPAAB_database %>%
dplyr::distinct(article_id) %>% # Returns a list of distinct article_id
nrow(.) # Returns the length of the current file (which is the list of distinct article_id)## [1] 901EIPAAB_database %>%
nrow(.) # Returns the length of the current file (which is the length of the whole datafile)## [1] 1740Each row represent a unique species by compound combination within a given article. This is represented by the column unique_row_id This is a combination of the extractors response id, specie,s and compound. For example, R_0Bqz2RQ4JxPfBkZ_Danio_rerio_Diazepam, response id = R_0Bqz2RQ4JxPfBkZ, species = Danio rerio, and compound = Diazepam
EIPAAB_database %>%
dplyr::select(unique_row_id) %>% # selects just the unique_column_id column
dplyr::arrange(unique_row_id) %>% # arranges the column alphabetically so the same examples will be given everytime
dplyr::slice(1:10) # Returns only the first 10 rows## unique_row_id
## 1 R_0Bqz2RQ4JxPfBkZ_Danio_rerio_Diazepam
## 2 R_0CHlDBs9ipt4suZ_Astyanax_mexicanus_Aripiprazole
## 3 R_0Ck0AOjLDWukBUt_Procambarus_clarkii_Chlordiazepoxide
## 4 R_0JvaI9dlvTbozUl_Daphnia_magna_Fluoxetine
## 5 R_0JvaI9dlvTbozUl_Daphnia_magna_Sertraline
## 6 R_0Srt7zn9MwHKne1_Danio_rerio_Escitalopram
## 7 R_0p8ZEROmCGlSR7r_Oryzias_latipes_Fluoxetine
## 8 R_10C0XxjAUoZmibO_Amphiprion_ocellaris_17-alpha-ethinylestradiol
## 9 R_10GdzsXlrkwamUt_Daphnia_magna_Cisplatin
## 10 R_10NOT0XWL5TXN5m_Coenagrion_hastulatum_DiphenhydramineNow the number of total treatments represented in the data, this is the total number of unique doses per species by compound combination.
In the map the number of treatments was only extracted for water-borne exposures, the NAs, represent other exposure routes. Therefore, the number of water-borne exposures treatments are 6294, and there are an additional 226 articles that don’t have treatment numbers. We know they all have at least two treatments, a control and a compound of interest, because that is part of the inclusion criteria. So we could add the number of NAs * 2 to the total, this would be 6746 total treatment groups. Although, this would likely be an underestimate of the true total.
EIPAAB_database %>%
dplyr::summarise(
groups = sum(compound_treatment_levels, na.rm = TRUE), # Calculate the sum of 'compound_treatment_levels' while ignoring NA values
nas = sum(is.na(compound_treatment_levels)), # Count the number of NA values in 'compound_treatment_levels'
total = groups + (nas * 2) # Calculate the total by adding 'groups' to twice the number of NAs
)## groups nas total
## 1 6154 226 660610 Study motivation
Let’s look at how the evidence collected breaks down by the three study motivations
total_atricles <- EIPAAB_database %>%
dplyr::distinct(article_id) %>%
nrow()
# Analyze the study motivations in the dataset
EIPAAB_database %>%
dplyr::group_by(article_id) %>% # Group the data by 'article_id'
dplyr::sample_n(1) %>% # Randomly sample one row from each group (i.e., each unique 'article_id')
dplyr::ungroup() %>% # Ungroup the data to remove the previous grouping
dplyr::group_by(study_motivation) %>% # Group the data by 'study_motivation'
dplyr::reframe( # Create a summary data frame with the count and percentage of each study motivation
n = length(study_motivation), # Count the number of occurrences of each study motivation
`%` = round(n / total_atricles, 3) * 100 # Calculate the percentage of total articles
) %>%
dplyr::arrange(desc(n)) # Arrange the resulting data frame in descending order of the count## # A tibble: 3 × 3
## study_motivation n `%`
## <chr> <int> <dbl>
## 1 Environmental 510 56.6
## 2 Medical 234 26
## 3 Basic research 157 17.4Here we are changing the order of these in the database to “Environmental”, “Medical”, “Basic research” for plots.
EIPAAB_database <- EIPAAB_database %>%
dplyr::mutate(study_motivation = fct_relevel(study_motivation, "Environmental", "Medical", "Basic research"))11 Publications by year
11.1 Number per year
Year range is 1974 to 2022, so 48 years worth of empirical research has contributed to this evidence base.
EIPAAB_database %>%
dplyr::reframe(min_year = min(year),
max_year = max(year),
total_years = max_year-min_year)## min_year max_year total_years
## 1 1974 2022 48Now making a summary for the number of publications per year based on study motivation
# Create a complete sequence of years and all unique study motivations
all_years <- as.character(1974:2022)
all_study_motivations <- unique(EIPAAB_database$study_motivation)
# Create a data frame with all combinations of year and study motivation
all_combinations <- expand.grid(year = all_years, study_motivation = all_study_motivations, stringsAsFactors = FALSE)
# Summarize the data
pub_year <- EIPAAB_database %>%
group_by(year, study_motivation) %>%
summarize(n = length(unique(article_id)), .groups = 'drop') %>%
mutate(year = as.character(year))
# Join with the complete grid of years and study motivations
pub_year_complete <- all_combinations %>%
left_join(pub_year, by = c("year", "study_motivation")) %>%
mutate(n = if_else(is.na(n), 0, n),
year = as.numeric(year))Here’s a summary figure for the manuscript (MS).
# Define the colour palette
motivation_colour_theme <- c("#60BD6C", "#D359A1", "#3C82C4") # Making colour theme to apply to plot
# Create the plot
pub_year_fig <- pub_year_complete %>%
# Group years before 1996 and reformat the year column
dplyr::mutate(
year = as.character(if_else(year < 1996, 1996, year)), # Grouping years before 1996
year = if_else(year == "1996", "<1997", year) # Renaming 1995 group to "<1996"
) %>%
# Creating the plot
ggplot(aes(y = n, x = year, fill = study_motivation)) +
geom_bar(stat = "identity", width = 0.9) +
# Apply the custom colours
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
# Customizing the theme
theme(
legend.position = c(0.05, 1), # Positioning the legend in the top-left corner within the plot
legend.justification = c(0, 1), # Ensuring the legend box aligns properly at the top-left corner
axis.text.x = element_text(angle = 90, vjust = 0.5) # Rotating x-axis labels for better readability
) +
# Adding axis labels
labs(
x = "Year of publication",
y = "Number of articles"
)## Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
## 3.5.0.
## ℹ Please use the `legend.position.inside` argument of `theme()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.# Display the plot
pub_year_fig
Saving the figure as a PDF
setwd(figures_path)
#ggsave("pub_year_fig.png", plot = pub_year_fig, width = 10, height = 5, dpi = 300) #if you want to save a png
ggsave("study_pub_year_fig.pdf", plot = pub_year_fig, width = 10, height = 5)11.2 Cumulative and relative growth
Making values for cumulative and relative growth in articles. This is the cumulative number of articles per year for each study moitvation, as well as the relative growth based on 2007. We selected 2007 for a 15 year overview in growth.
pub_year_growth <- pub_year_complete %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(
n_cumulative = cumsum(n), # Calculate the cumulative sum of 'n'
n_cumulative_prop = n_cumulative / max(n_cumulative), # Calculate the cumulative proportion
n_2007 = ifelse(year == 2007, n, NA_real_), # Get n value for year 2007
n_2007 = first(na.omit(n_2007)), # Propagate the n_2012 value within the group
n_ratio_to_2007 = n / n_2007 # Calculate number of articles relative to that of 2007
) %>%
dplyr::ungroup() %>%
dplyr::select(study_motivation, year, n, n_cumulative, n_cumulative_prop, n_ratio_to_2007)Making a plot for each motivation cumulative growth since 1974 (the first identified study)
cumulative_articles_fig <- pub_year_growth %>%
ggplot(aes(y = n_cumulative, x = year, colour = study_motivation)) +
geom_line(stat = "identity", linewidth = 1.5) +
geom_hline(yintercept = 0) +
scale_x_continuous(breaks = seq(1974, 2022, by = 1)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
# Customizing the theme
theme(
legend.position = c(0.05, 1), # Positioning the legend in the top-left corner within the plot
legend.justification = c(0, 1), # Ensuring the legend box aligns properly at the top-left corner
axis.text.x = element_text(angle = 90, vjust = 0.5) # Rotating x-axis labels for better readability
) +
# Adding axis labels
labs(
x = "Year of publication",
y = "Articles cumulative growth"
)
cumulative_articles_fig
setwd(figures_path)
ggsave("study_cumulative_articles_fig.pdf", plot = cumulative_articles_fig, width = 10, height = 5)11.3 Reltive growth
Let’s look at relative growth compared to the research area more broadly
I will identify the most common research area based on each study motivation.
Environmental motivation = Environmental Sciences & Ecology Medical motivation = Neurosciences & Neurology Basic research = Neurosciences & Neurology
EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(study_motivation, wos_research_areas) %>%
dplyr::reframe(n = length(doi)) %>%
dplyr::mutate(wos_research_areas = str_trim(wos_research_areas)) %>%
tidyr::separate_rows(wos_research_areas, sep = ";") %>%
dplyr::mutate(wos_research_areas = str_trim(wos_research_areas)) %>%
dplyr::group_by(study_motivation, wos_research_areas) %>%
dplyr::reframe(n_total = sum(n)) %>%
dplyr::arrange(desc(n_total)) %>%
dplyr::arrange(study_motivation) %>%
dplyr::group_by(study_motivation) %>%
dplyr::slice(1:2) %>%
dplyr::ungroup()## # A tibble: 6 × 3
## study_motivation wos_research_areas n_total
## <fct> <chr> <int>
## 1 Environmental Environmental Sciences & Ecology 321
## 2 Environmental Toxicology 193
## 3 Medical Neurosciences & Neurology 106
## 4 Medical Pharmacology & Pharmacy 72
## 5 Basic research Neurosciences & Neurology 56
## 6 Basic research Behavioral Sciences 43We will now compare the proportion cumulative growth of each study motivation against the most common research areas based on WoS.
I have searched articles published within these research areas from 1992-2022, and create a database to compare against.
Each search indued only a date range (e.g. PY=(1992-2021)) AND the given web of science resarch area (e.g. WC=(Pharmacology & Pharmacy)). Searchers were done on the 04/07/2024. Only the total number of articles each year was taken.
First we will import the research field annual number of articles database I create (martin-et-al-supp-file-9-wos-research-areas-1992-2022.csv). It is provide as supplementary file 9.
setwd(input_path)
wos_research_areas_n <- read.csv("martin-et-al-supp-file-9-wos-research-areas-1992-2022.csv") %>%
dplyr::arrange(year) %>%
dplyr::group_by(research_area) %>%
dplyr::mutate(
n_cumulative = cumsum(n), # Calculate the cumulative sum of 'n'
n_cumulative_prop = n_cumulative / max(n_cumulative), # Calculate the cumulative proportion
n_2007 = ifelse(year == 2007, n, NA_real_), # Get n value for year 2011
n_2007= first(na.omit(n_2007)), # Propagate the n_2011 value within the group
n_ratio_to_2007 = n / n_2007 # Calculate n_ratio_to_2000
) %>%
dplyr::ungroup() %>%
dplyr::select(research_area, year, n, n_cumulative, n_cumulative_prop, n_ratio_to_2007)Combined the number of articles with those in the EIPAAB database
pub_year_growth_comp <- pub_year_growth %>%
dplyr::rename(research_area = study_motivation)
wos_research_areas_comp <- wos_research_areas_n %>%
rbind(., pub_year_growth_comp) %>%
dplyr::mutate(research_area = factor(research_area, levels=c("Environmental", "Medical", "Basic research",
"Environmental Sciences and Ecology", "Toxicology",
"Neurosciences and Neurology", "Pharmacology and Pharmacy",
"All Research Areas")))Let’s see what the relative growth was in 2022 (the latest included year in the evidence base)
wos_research_areas_comp %>%
dplyr::filter(year == 2022)## # A tibble: 8 × 6
## research_area year n n_cumulative n_cumulative_prop n_ratio_to_2007
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Pharmacology and … 2022 8.04e4 1458833 1 1.77
## 2 Neurosciences and… 2022 2.44e4 450515 1 1.60
## 3 Toxicology 2022 1.69e4 391858 1 1.41
## 4 Environmental Sci… 2022 4.60e3 103123 1 1.07
## 5 All Research Areas 2022 3.62e6 66839129 1 1.79
## 6 Environmental 2022 5.7 e1 510 1 19
## 7 Medical 2022 3 e1 234 1 10
## 8 Basic research 2022 9 e0 158 1 2.2511.3.1 Enviormental relative growth
Let’s now compare the relative growth in Environmental research
# Define the colour palette
env_colour_theme <- c("#60BD6C", "#2E4B22", "black") # Making colour theme to apply to plot
enviro_comp <- c("Environmental", "Environmental Sciences and Ecology", "All Research Areas")
line_types <- c("Environmental" = "solid",
"Environmental Sciences and Ecology" = "dashed",
"All Research Areas" = "solid")
relative_growth_env_fig <- wos_research_areas_comp %>%
dplyr::filter(research_area %in% enviro_comp, year > 2006) %>%
ggplot(aes(y = n_ratio_to_2007, x = year, colour = research_area, linetype = research_area)) +
geom_line(stat = "identity", linewidth = 1.5) +
scale_x_continuous(breaks = seq(2007, 2022, by = 1)) +
scale_y_continuous(limits = c(0, 22), breaks = seq(0, 22, by = 4)) + # Scale Y axis from 0 to 22
scale_colour_manual(values = env_colour_theme, name = "Research Area") +
scale_linetype_manual(values = line_types) + # Apply manual line types
guides(linetype = "none") + # Remove legend for line types
theme_classic() +
# Customizing the theme
theme(
legend.position = c(0.05, 1), # Positioning the legend in the top-left corner within the plot
legend.justification = c(0, 1), # Ensuring the legend box aligns properly at the top-left corner
axis.text.x = element_text(angle = 90, vjust = 0.5) # Rotating x-axis labels for better readability
) +
# Adding axis labels
labs(
x = "Year of publication",
y = "Relative growth compared to 2007 (15 year baseline)"
)
relative_growth_env_fig
setwd(figures_path)
ggsave("study_relative_growth_env_fig.pdf", plot = relative_growth_env_fig, width = 5, height = 5)11.3.2 Medical relative growth
Comparing the relative growth in medical research
# Define the colour palette
med_colour_theme <- c("#D359A1", "#D2137F", "black") # Making colour theme to apply to plot
med_comp <- c("Medical", "Neurosciences and Neurology", "All Research Areas")
line_types <- c("Medical" = "solid",
"Neurosciences and Neurology" = "dashed",
"All Research Areas" = "solid")
relative_growth_med_fig <- wos_research_areas_comp %>%
dplyr::filter(research_area %in% med_comp, year > 2006) %>%
ggplot(aes(y = n_ratio_to_2007, x = year, colour = research_area, linetype = research_area)) +
geom_line(stat = "identity", linewidth = 1.5) +
scale_x_continuous(breaks = seq(2007, 2022, by = 1)) +
scale_y_continuous(limits = c(0, 22), breaks = seq(0, 22, by = 4)) + # Scale Y axis from 0 to 22
scale_colour_manual(values = med_colour_theme, name = "Research Area") +
scale_linetype_manual(values = line_types) + # Apply manual line types
guides(linetype = "none") + # Remove legend for line types
theme_classic() +
# Customizing the theme
theme(
legend.position = c(0.05, 1), # Positioning the legend in the top-left corner within the plot
legend.justification = c(0, 1), # Ensuring the legend box aligns properly at the top-left corner
axis.text.x = element_text(angle = 90, vjust = 0.5) # Rotating x-axis labels for better readability
) +
# Adding axis labels
labs(
x = "Year of publication",
y = "Relative growth compared to 2007 (15 year baseline)"
)
relative_growth_med_fig
setwd(figures_path)
ggsave("study_relative_growth_med_fig.pdf", plot = relative_growth_med_fig, width = 5, height = 5)11.3.2 Basic resarch relative growth
Comparing relative growth in basic research
# Define the colour palette
basic_colour_theme <- c("#3C82C4", "#26276D", "black") # Making colour theme to apply to plot
basic_comp <- c("Basic research", "Neurosciences and Neurology", "All Research Areas")
line_types <- c("Basic research" = "solid",
"Neurosciences and Neurology" = "dashed",
"All Research Areas" = "solid")
relative_growth_base_fig <- wos_research_areas_comp %>%
dplyr::filter(research_area %in% basic_comp, year > 2006) %>%
ggplot(aes(y = n_ratio_to_2007, x = year, colour = research_area, linetype = research_area)) +
geom_line(stat = "identity", linewidth = 1.5) +
scale_x_continuous(breaks = seq(2007, 2022, by = 1)) +
scale_y_continuous(limits = c(0, 22), breaks = seq(0, 22, by = 4)) + # Scale Y axis from 0 to 22
scale_colour_manual(values = basic_colour_theme, name = "Research Area") +
scale_linetype_manual(values = line_types) + # Apply manual line types
guides(linetype = "none") + # Remove legend for line types
theme_classic() +
# Customizing the theme
theme(
legend.position = c(0.05, 1), # Positioning the legend in the top-left corner within the plot
legend.justification = c(0, 1), # Ensuring the legend box aligns properly at the top-left corner
axis.text.x = element_text(angle = 90, vjust = 0.5) # Rotating x-axis labels for better readability
) +
# Adding axis labels
labs(
x = "Year of publication",
y = "Relative growth compared to 2007 (15 year baseline)"
)
relative_growth_base_fig
setwd(figures_path)
ggsave("study_relative_growth_base_fig.pdf", plot = relative_growth_base_fig, width = 5, height = 5)12 Population, Exposure and Outcome
Looking at the link between the PEO elements. What was the average study design.
Below I have made a table that groups by these elements to see the average study design
It was 1 compound, 1 species and 1 behavioural class (41%)
behav_boolean <- c("behav_movement_boolean", "behav_boldness_boolean", "behav_foraging_boolean", "behav_antipredator_boolean", "behav_mating_boolean", "behav_post_mating_boolean", "behav_agression_boolean", "behav_sociality_boolean", "behav_cognition_boolean", "behav_noncat_boolean")
EIPAAB_database %>%
dplyr::mutate(behav_n = rowSums(across(all_of(behav_boolean)), na.rm = TRUE)) %>% # how many behav class measured
dplyr::group_by(article_id) %>%
dplyr::arrange(desc(behav_n)) %>%
dplyr::slice(1) %>%
dplyr::ungroup() %>%
dplyr::select(compound_n, species_n, behav_n) %>%
dplyr::group_by(compound_n, species_n, behav_n) %>%
dplyr::reframe(n= length(compound_n),
'%' = round(n/902*100,1)) %>%
dplyr::arrange(desc(n))## # A tibble: 45 × 5
## compound_n species_n behav_n n `%`
## <int> <int> <dbl> <int> <dbl>
## 1 1 1 1 374 41.5
## 2 1 1 2 160 17.7
## 3 2 1 1 78 8.6
## 4 1 1 3 63 7
## 5 3 1 1 45 5
## 6 2 1 2 34 3.8
## 7 4 1 1 24 2.7
## 8 3 1 2 15 1.7
## 9 1 2 1 13 1.4
## 10 5 1 1 9 1
## # ℹ 35 more rowsI summary df that has the number of PEO elements in each of the 901 studies
PEO_element_summary <- EIPAAB_database %>%
dplyr::mutate(behav_n = rowSums(across(all_of(behav_boolean)), na.rm = TRUE)) %>%
dplyr::group_by(article_id) %>%
dplyr::arrange(desc(behav_n)) %>%
dplyr::slice(1) %>%
dplyr::ungroup() %>%
dplyr::select(compound_n, species_n, behav_n)Looking at the number of species used
PEO_element_summary %>%
dplyr::group_by(species_n) %>%
dplyr::reframe(n = length(species_n),
'%' = n/901)## # A tibble: 5 × 3
## species_n n `%`
## <int> <int> <dbl>
## 1 1 873 0.969
## 2 2 25 0.0277
## 3 3 1 0.00111
## 4 4 1 0.00111
## 5 5 1 0.00111Looking at the number of compounds used
PEO_element_summary %>%
dplyr::group_by(compound_n) %>%
dplyr::reframe(n = length(compound_n),
'%' = n/901)## # A tibble: 18 × 3
## compound_n n `%`
## <int> <int> <dbl>
## 1 1 624 0.693
## 2 2 127 0.141
## 3 3 67 0.0744
## 4 4 32 0.0355
## 5 5 16 0.0178
## 6 6 8 0.00888
## 7 7 6 0.00666
## 8 8 5 0.00555
## 9 9 1 0.00111
## 10 10 2 0.00222
## 11 11 3 0.00333
## 12 12 2 0.00222
## 13 13 2 0.00222
## 14 14 2 0.00222
## 15 16 1 0.00111
## 16 18 1 0.00111
## 17 25 1 0.00111
## 18 52 1 0.00111PEO_element_summary %>%
dplyr::group_by(behav_n) %>%
dplyr::reframe(n = length(species_n),
'%' = n/901)## # A tibble: 6 × 3
## behav_n n `%`
## <dbl> <int> <dbl>
## 1 1 583 0.647
## 2 2 227 0.252
## 3 3 78 0.0866
## 4 4 10 0.0111
## 5 5 2 0.00222
## 6 7 1 0.0011113 Species
Let’s take a closer look at species information
13.1 Number of distict species
There are 173 species in the EIPAAB database
EIPAAB_database %>%
dplyr::distinct(species_name) %>%
nrow()## [1] 173The number of spp each study motivation
EIPAAB_database %>%
dplyr::group_by(study_motivation) %>%
dplyr::reframe(n_spp = length(unique(species_name)),
total_study = length(unique(article_id)),
rel_n = n_spp/total_study)## # A tibble: 3 × 4
## study_motivation n_spp total_study rel_n
## <fct> <int> <int> <dbl>
## 1 Environmental 143 510 0.280
## 2 Medical 26 234 0.111
## 3 Basic research 43 158 0.272There are 21 class
EIPAAB_database %>%
dplyr::distinct(species_class) %>%
nrow()## [1] 21There are 935 different groups of animals used across all 901 studies (i.e. some studies had more then one species)
EIPAAB_database %>%
dplyr::distinct(unique_population_id) %>%
nrow(.)## [1] 93513.2 Major taxa groups
13.2.1 Cladogram (Taxonomic tree)
Let’s make a Cladogram to get an overview of what taxa are in the database
spp_taxonomy <- EIPAAB_database %>%
dplyr::group_by(species_name) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::filter(!is.na(species_family), !str_detect(species_species, "spp."), species_kingdom != "Chromista") %>%
dplyr::select("species_kingdom", "species_phylum", "species_class", "species_order",
"species_family", "species_genus", "species_species") %>%
dplyr::mutate(species_species = paste0(substr(species_genus, 1, 1), ". ", sub("^[^ ]+ ", "", species_species)))
# The mutate changes the spp name to abbreviate the Genus (e.g. Aeshna cyanea to A. cyanea)Create a hierarchical structure for the plot
taxonomy <- spp_taxonomy[, c("species_kingdom", "species_phylum", "species_class", "species_order", "species_family", "species_genus", "species_species")]
taxonomy[] <- lapply(taxonomy, factor)
# Create a phylogenetic tree
phylo_tree <- as.phylo.formula(~species_kingdom/species_phylum/species_class/species_order/species_family/species_genus/species_species, data = taxonomy)Manual creating a phylo_tree (with equal branches)
ggtree_obj <- ggtree(phylo_tree, branch.length='none', layout='circular')
# Extract the phylum information for coloring
#taxonomy$label <- paste0(substr(spp_taxonomy$species_genus, 1, 1), ". ", spp_taxonomy$species_species)
class_info <- taxonomy$species_class[match(phylo_tree$tip.label, taxonomy$species_species)]
# Add the phylum information to the ggtree object
ggtree_obj <- ggtree_obj %<+% data.frame(label = phylo_tree$tip.label, class = class_info)## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.# Create a color vector for the phylum levels
class_colors <- rainbow(length(unique(class_info)))
names(class_colors) <- unique(class_info)
# Plot the cladogram with colored branches
spp_cladogram <- ggtree_obj +
geom_tiplab(size=3) + #If you want to add species names
geom_tree(aes(color=class)) +
scale_color_manual(values = class_colors) +
theme(legend.position = "right")spp_cladogram
setwd(figures_path)
ggsave("spp_cladogram.pdf", plot = spp_cladogram, width = 8, height = 5)13.2.2 Class level
Let’s group by class to see the major taxonomic Classes used
First removing cases where species_species was “spp.” replacing with NA for taxonomic classification
EIPAAB_database <- EIPAAB_database %>%
dplyr::mutate(species_species = if_else(species_species == "spp.", NA, species_species))Let’s look at the major Class
# Total number of spp
n_spp <- EIPAAB_database %>%
dplyr::distinct(species_name) %>%
nrow()
# Number of spp per Class and per phylum
spp_classes <- EIPAAB_database %>%
dplyr::group_by(species_class, species_phylum) %>%
dplyr::reframe(count_class = length(unique(species_name)),
percent_class = round(count_class/n_spp*100,1)) %>%
dplyr::group_by(species_phylum) %>%
dplyr::mutate(count_phylum = sum(count_class),
percent_phylum = round(count_phylum/n_spp*100,1)) %>%
dplyr::ungroup() %>%
dplyr::arrange(desc(percent_class))
spp_classes %>%
dplyr::slice(1:10)## # A tibble: 10 × 6
## species_class species_phylum count_class percent_class count_phylum
## <chr> <chr> <int> <dbl> <int>
## 1 Actinopterygii Chordata 71 41 87
## 2 Malacostraca Arthropoda 21 12.1 42
## 3 Gastropoda Mollusca 19 11 28
## 4 Amphibia Chordata 12 6.9 87
## 5 Branchiopoda Arthropoda 10 5.8 42
## 6 Bivalvia Mollusca 8 4.6 28
## 7 Insecta Arthropoda 8 4.6 42
## 8 Rhabditophora Platyhelminthes 5 2.9 6
## 9 Reptilia Chordata 3 1.7 87
## 10 Copepoda Arthropoda 2 1.2 42
## # ℹ 1 more variable: percent_phylum <dbl>Making a figure for the 15 most abundant Class, it terms of species diversity in the EIPAAB database
class_n_spp_fig <- spp_classes %>%
dplyr::arrange(desc(percent_class)) %>% # arrange the dataset
dplyr::slice(1:15) %>% # Take only the most diverse 15 Class
dplyr::mutate(species_class = fct_reorder(species_class, percent_class), # Order by diversity
species_phylum = fct_reorder(species_phylum, desc(percent_phylum))) %>% # Order by diversity
ggplot(aes(x=species_class, y=count_class, color = species_phylum)) +
geom_segment(aes(x=species_class, xend=species_class, y=0, yend=count_class)) +
geom_point(size=4) +
geom_text(aes(label = count_class),
hjust=-1.2,
size=3.5,
color="black") +
scale_colour_brewer(palette= "Dark2") +
coord_flip() +
ylim(0, 75) +
theme_classic() +
labs(
x = "",
y = "Number of distict species in the database"
) +
theme(legend.position = c(0., 0.05), # Positioning the legend inside the plot
legend.justification = c(-3, 0), # Bottom left inside the plot
legend.box.just = "right",
legend.background = element_rect(fill=alpha('white', 0.5))
)
class_n_spp_fig
Save the figure
setwd(figures_path)
ggsave("spp_class_n_spp_fig.pdf", plot = class_n_spp_fig, width = 5, height = 5)13.2.3 Phylum level
Here’s a look at the % at the phylum level
# In the class summary we also included percent_phylum
spp_classes %>%
dplyr::group_by(species_phylum) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::select(-species_class, -count_class, -percent_class) %>%
dplyr::arrange(desc(percent_phylum))## # A tibble: 8 × 3
## species_phylum count_phylum percent_phylum
## <chr> <int> <dbl>
## 1 Chordata 87 50.3
## 2 Arthropoda 42 24.3
## 3 Mollusca 28 16.2
## 4 Platyhelminthes 6 3.5
## 5 Annelida 3 1.7
## 6 Echinodermata 3 1.7
## 7 Cnidaria 2 1.2
## 8 Rotifera 2 1.2A ring chart at the phylum level
ring_plot_df <- spp_classes %>%
dplyr::slice(1:15) %>%
dplyr::group_by(species_phylum) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::select(species_phylum, count_phylum) %>%
dplyr::mutate(percent_phylum = count_phylum/sum(count_phylum)) %>%
dplyr::arrange(desc(percent_phylum))
# Compute the cumulative percentages (top of each rectangle)
ring_plot_df$ymax = cumsum(ring_plot_df$percent_phylum)
# Compute the bottom of each rectangle
ring_plot_df$ymin = c(0, head(ring_plot_df$ymax, n=-1))
# Compute label position
ring_plot_df$labelPosition <- (ring_plot_df$ymax + ring_plot_df$ymin) / 2
# Compute a good label
ring_plot_df$label <- paste0(ring_plot_df$species_phylum, "\n (n = ", ring_plot_df$count_phylum, ")")
phylum_ring_fig <- ring_plot_df %>%
dplyr::mutate(species_phylum = fct_reorder(species_phylum, desc(percent_phylum))) %>%
ggplot(aes(ymax=ymax, ymin=ymin, xmax=4, xmin=3, fill=species_phylum)) +
geom_rect() +
coord_polar(theta="y") +
geom_label(x=5, aes(y=labelPosition, label=label), size=3, alpha = 0.8) +
scale_fill_brewer(palette= "Dark2") +
xlim(c(2, 5)) +
theme_void() +
theme(legend.position = "none")
phylum_ring_fig
setwd(figures_path)
ggsave("spp_phylum_ring_fig.pdf", plot = phylum_ring_fig, width = 5, height = 5)13.3 Taxa representation
Now we will look at how many times each phylum, class, order, family, genus, species appear in the database
First we will make a dataset that counts the number of species within each phylum, class, order, family, and genus.
# Step 1: Separate and pivot the data
lineage_data <- EIPAAB_database %>%
pivot_longer(cols = c("species_phylum", "species_class", "species_order",
"species_family", "species_genus", "species_species"),
names_to = "lineage_level", values_to = "classification") %>%
dplyr::mutate(lineage_level = str_remove(lineage_level, "species_"))
# Define the order
lineage_levels_order <- c("phylum", "class", "order", "family", "genus", "species")
# Step 2: Create parent-child relationships
lineage_data <- lineage_data %>%
group_by(unique_row_id) %>%
mutate(parent = case_when(
lineage_level == "phylum" ~ "Animalia",
lineage_level == "class" ~ lag(classification, 1),
lineage_level == "order" ~ lag(classification, 1),
lineage_level == "family" ~ lag(classification, 1),
lineage_level == "genus" ~ lag(classification, 1),
lineage_level == "species" ~ lag(classification, 1),
)) %>%
ungroup()Here we sum the total number of species used in the database across each taxonomic classification
n_rows <- EIPAAB_database %>%
nrow()
lineage_count_use <- lineage_data %>%
dplyr::group_by(classification, lineage_level, parent) %>%
dplyr::reframe(classification_count = length(unique_row_id),
classification_percent = round(classification_count/n_rows*100,1))Making a plot to look at the 15 most commonly used class, but you can do this at any of the taxonomic levels
class_use_fig <- lineage_count_use %>%
dplyr::filter(lineage_level == "class") %>%
dplyr::arrange(desc(classification_percent)) %>%
dplyr::slice(1:15) %>%
dplyr::mutate(classification = fct_reorder(classification, classification_percent),
parent = fct_reorder(parent, desc(classification_percent))) %>%
ggplot(aes(x=classification, y=classification_percent, color = parent)) +
geom_segment(aes(x=classification, xend=classification, y=0, yend=classification_percent)) +
geom_point(size=4) +
geom_text(aes(label = paste0(round(classification_percent,2), "%")),
hjust=-0.5,
size=3.5,
color="black") +
scale_colour_brewer(palette= "Dark2") +
coord_flip() +
ylim(0, 100) +
theme_classic() +
labs(
x = "",
y = "Percentage representation in the database"
) +
theme(legend.position = c(0., 0.05), # Positioning the legend inside the plot
legend.justification = c(-3, 0), # Bottom left inside the plot
legend.box.just = "right",
legend.background = element_rect(fill=alpha('white', 0.5))
)
class_use_fig
setwd(figures_path)
ggsave("spp_class_use_fig.pdf", plot = class_use_fig, width = 5, height = 5)lineage_count_use %>%
dplyr::filter(lineage_level == "class") %>%
dplyr::group_by(classification) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::arrange(desc(classification_percent))## # A tibble: 21 × 5
## classification lineage_level parent classification_count
## <chr> <chr> <chr> <int>
## 1 Actinopterygii class Chordata 1312
## 2 Branchiopoda class Arthropoda 130
## 3 Gastropoda class Mollusca 69
## 4 Malacostraca class Arthropoda 61
## 5 Amphibia class Chordata 44
## 6 Rhabditophora class Platyhelminthes 29
## 7 Bivalvia class Mollusca 25
## 8 Hydrozoa class Cnidaria 22
## 9 Insecta class Arthropoda 17
## 10 Cephalopoda class Mollusca 7
## # ℹ 11 more rows
## # ℹ 1 more variable: classification_percent <dbl>Here’s break down by Phylum
ring_use_plot_df <- lineage_count_use %>%
dplyr::filter(lineage_level == "phylum") %>%
dplyr::arrange(desc(classification_percent)) %>%
dplyr::mutate(classification_percent = round(classification_percent, 2)) %>%
dplyr::slice(1:8)
# Compute the cumulative percentages (top of each rectangle)
ring_use_plot_df$ymax = cumsum(ring_use_plot_df$classification_percent)
# Compute the bottom of each rectangle
ring_use_plot_df$ymin = c(0, head(ring_use_plot_df$ymax, n=-1))
# Compute label position
ring_use_plot_df$labelPosition <- (ring_use_plot_df$ymax + ring_use_plot_df$ymin) / 2
# Compute a good label
ring_use_plot_df$label <- paste0(ring_use_plot_df$classification, "\n (", ring_use_plot_df$classification_percent, "%)")
phylum_use_ring_fig <- ring_use_plot_df %>%
dplyr::mutate(classification = fct_reorder(classification, desc(classification_percent))) %>%
ggplot(aes(ymax=ymax, ymin=ymin, xmax=4, xmin=3, fill=classification)) +
geom_rect() +
coord_polar(theta="y") +
geom_label(x=5, aes(y=labelPosition, label=label), size=3, alpha = 0.8) +
scale_fill_brewer(palette= "Dark2") +
xlim(c(2, 5)) +
theme_void()
#theme(legend.position = "none")
phylum_use_ring_fig
setwd(figures_path)
ggsave("spp_phylum_use_ring_fig.pdf", plot = phylum_use_ring_fig, width = 5, height = 5)13.3.2 Taxa representation by study motivation
Making a data set that looks at relative representation by each motivation, and the total
lineage_count_use_motivation <- lineage_data %>%
dplyr::group_by(study_motivation, lineage_level, parent, classification) %>%
dplyr::reframe(classification_count = length(unique_row_id)) %>%
dplyr::group_by(study_motivation, lineage_level) %>%
dplyr::mutate(n_motivation = sum(classification_count)) %>%
dplyr::ungroup() %>%
dplyr::mutate(classification_percent = (classification_count/n_motivation)*100)
lineage_count_use_all <- lineage_data %>%
dplyr::group_by(lineage_level, parent, classification) %>%
dplyr::reframe(classification_count = length(unique_row_id)) %>%
dplyr::group_by(lineage_level) %>%
dplyr::mutate(n_motivation = sum(classification_count)) %>%
dplyr::ungroup() %>%
dplyr::mutate(classification_percent = (classification_count/n_motivation)*100) %>%
dplyr::mutate(study_motivation = "All")
lineage_count_use_motivation <- lineage_count_use_motivation %>%
rbind(., lineage_count_use_all) %>%
dplyr::mutate(study_motivation = fct_relevel(study_motivation, "All", "Environmental", "Medical", "Basic research"))Here’s a tile plot to compare the use of different taxa across the study motivations
class_order_df <- lineage_count_use %>%
dplyr::filter(lineage_level == "class") %>%
dplyr::arrange(classification_percent) %>%
dplyr::mutate(class_order = 1:nrow(.)) %>%
dplyr::select(classification, class_order)
class_use_motivation_fig <- lineage_count_use_motivation %>%
dplyr::filter(lineage_level == "class") %>%
dplyr::full_join(., class_order_df, by = "classification") %>%
dplyr::mutate(classification = fct_reorder(classification, class_order),
parent = fct_reorder(parent, desc(class_order))) %>%
ggplot(aes(x = study_motivation, y = classification, fill = classification_percent)) +
geom_tile() +
geom_text(aes(label = paste0(round(classification_percent, 1),"%"))) +
scale_fill_gradient(name = expression("Relative\nabudance (%)"),
low = "#FFFFFF", high = "#231F20") +
theme_classic() +
# theme(axis.text.x = element_text(angle = 90, hjust = 1.1, vjust = 0.4)) +
labs(x = "Study motivation",
y = "Taxonomic class")
class_use_motivation_fig
setwd(figures_path)
ggsave("spp_class_use_motivation_fig.pdf", plot = class_use_motivation_fig, width = 5, height = 5)13.3.3 Most common species
Let’s see what the most common species were. This is calculated at the population level (i.e. doesn’t count each species mutiple times if multiple compounds were used in a single article; unique_population_id)
n_total <- EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>%
dplyr::reframe(n = length(unique_population_id)) %>%
nrow(.)
n_spp <- EIPAAB_database %>%
dplyr::group_by(unique_population_id, species_ncbi_taxonomy_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(species_name, species_ncbi_taxonomy_id) %>%
dplyr::reframe(n = length(species_name),
percent = round(n/n_total*100,1)) %>%
dplyr::arrange(desc(n)) %>%
dplyr::mutate(spp_number = 1:nrow(.))
n_spp## # A tibble: 173 × 5
## species_name species_ncbi_taxonomy_id n percent spp_number
## <chr> <chr> <int> <dbl> <int>
## 1 Danio rerio NCBI:txid7955 412 44.1 1
## 2 Daphnia magna NCBI:txid35525 54 5.8 2
## 3 Pimephales promelas NCBI:txid90988 32 3.4 3
## 4 Betta splendens NCBI:txid158456 26 2.8 4
## 5 Poecilia reticulata NCBI:txid8081 26 2.8 5
## 6 Gambusia holbrooki NCBI:txid37273 18 1.9 6
## 7 Carassius auratus NCBI:txid7957 16 1.7 7
## 8 Oryzias latipes NCBI:txid8090 16 1.7 8
## 9 Gasterosteus aculeatus NCBI:txid69293 14 1.5 9
## 10 Oncorhynchus mykiss NCBI:txid8022 12 1.3 10
## # ℹ 163 more rowsMaking a broad category of abundance (article_n_group) to make the summary and figure more digestible
n_spp_fig_data <- n_spp %>%
dplyr::mutate(species_ncbi_taxonomy_id = fct_reorder(species_ncbi_taxonomy_id, desc(n))) %>%
dplyr::mutate(
article_n_group = case_when(
n == 1 ~ "One only",
n >= 2 & n <= 5 ~ "Between 2 and 5",
n >= 5 & n <= 10 ~ "Between 6 and 10",
n >= 10 ~ "Greater than 10",
TRUE ~ "Others"
)
)This is the number of species in each category
article_n_group_summary <- n_spp_fig_data %>%
dplyr::group_by(article_n_group) %>%
dplyr::reframe(n_cat = length(species_name))
article_n_group_summary## # A tibble: 4 × 2
## article_n_group n_cat
## <chr> <int>
## 1 Between 2 and 5 53
## 2 Between 6 and 10 6
## 3 Greater than 10 11
## 4 One only 103Making a plot to show the distribution of species use in the EIPAAB database
n_spp_fig <- n_spp_fig_data %>%
dplyr::mutate(article_n_group = fct_relevel(article_n_group, "Greater than 10", "Between 6 and 10", "Between 2 and 5", "One only")) %>%
ggplot(aes(y = n, x = spp_number, color = article_n_group)) +
geom_line(linewidth = 1, alpha = 0.2) +
geom_point(stat = "identity", size = 1, alpha = 0.8) +
scale_color_manual(
values = c(
"One only" = "#E94039",
"Between 2 and 5" = "#F18E76",
"Between 6 and 10" = "#877FBC",
"Greater than 10" = "#4D479D"
),
labels = c(
"One only" = "One only (n = 104)",
"Between 2 and 5" = "Between 2 and 5 (n = 53)",
"Between 6 and 10" = "Between 6 and 10 (n = 11)",
"Greater than 10" = "Greater than 10 (n = 6)"
)
) + # Set colours for each category
theme_classic() +
theme(
legend.position = c(-0.3, 0.4), # Positioning the legend inside the plot
legend.justification = c(-3, 0), # Bottom left inside the plot
legend.box.just = "right"
) +
labs(
x = paste0("Species (1-174)"),
y = "Number of articles",
color = "Article number category"
)
n_spp_fig
setwd(figures_path)
ggsave("spp_n_spp_fig.pdf", plot = n_spp_fig, width = 5, height = 5)Making a list of the most common 15 species
n_spp_used <- EIPAAB_database %>%
dplyr::distinct(unique_population_id) %>%
nrow(.)
top_15_spp_list <- EIPAAB_database %>%
dplyr::group_by(species_name) %>%
dplyr::summarise(n = length(unique(unique_population_id)), .groups = 'drop') %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:15) %>%
dplyr::pull(species_name) %>%
as.list()Making a summary dataframe base on study motivation
common_species <- EIPAAB_database %>%
dplyr::filter(species_name %in% top_15_spp_list) %>%
dplyr::group_by(species_name, study_motivation) %>%
dplyr::summarise(n = length(unique(article_id)), .groups = 'drop') %>%
tidyr::complete(species_name, study_motivation, fill = list(n = 0))
species_order <- common_species %>%
group_by(species_name) %>%
summarise(total_n = sum(n), .groups = 'drop') %>%
arrange(desc(total_n)) %>%
ungroup()
common_species <- common_species %>%
inner_join(species_order, by = "species_name") %>%
mutate(species_name = fct_reorder(species_name, total_n),
study_motivation = fct_relevel(study_motivation, "Environmental", "Medical", "Basic research")) A plot of the number of times each of the 15 overall most common species appeared in articles within the EIPAAB databse by study motivation. It’s a little hard to see in the chunk output, try viewing in an external window.
top_15_spp_fig <- common_species %>%
ggplot(aes(x=species_name, y=n, colour = study_motivation, fill = study_motivation, group = study_motivation)) +
geom_col(position = position_dodge(width = 0.8), width = 0.1) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_text(aes(label = n), hjust=-0.6, size=3.5, color="black", position = position_dodge(width = 0.8)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
coord_flip() +
theme_classic() +
labs(
x = "",
y = "Number of studies"
) +
theme()
top_15_spp_fig
setwd(figures_path)
ggsave("spp_top_15_spp_fig.pdf", plot = top_15_spp_fig, width = 5, height = 10)Summarising the top 10 in each motivation more specifically
spp_moitivation_summary <- EIPAAB_database %>%
dplyr::group_by(species_name, study_motivation) %>%
dplyr::summarise(n = length(unique(unique_population_id)), .groups = 'drop') %>%
tidyr::complete(species_name, study_motivation, fill = list(n = 0)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(prop = n/total) %>%
dplyr::select(-total)This plot shows the top 10 in each motivation
top_10_env_spp <- spp_moitivation_summary %>%
dplyr::filter(study_motivation == "Environmental") %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:10) %>%
dplyr::mutate(species_name = fct_reorder(species_name, n)) %>%
ggplot(aes(x=species_name, y=n)) +
geom_col(width = 0.01, colour = "#60BD6C", fill = "#60BD6C") +
geom_point(size = 2, colour = "#60BD6C", fill = "#60BD6C") +
geom_text(aes(label = n), hjust=-0.6, size=3.5, color="black") +
coord_flip() +
theme_classic() +
labs(
x = "",
y = "Number of studies",
title = "Environmental"
) +
theme(
plot.title = element_text(size = 12)
)
top_10_med_spp <- spp_moitivation_summary %>%
dplyr::filter(study_motivation == "Medical") %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:10) %>%
dplyr::mutate(species_name = fct_reorder(species_name, n)) %>%
ggplot(aes(x=species_name, y=n)) +
geom_col(width = 0.01, colour = "#D359A1", fill = "#D359A1") +
geom_point(size = 2, colour = "#D359A1", fill = "#D359A1") +
geom_text(aes(label = n), hjust=-0.6, size=3.5, color="black") +
coord_flip() +
theme_classic() +
labs(
x = "",
y = "Number of studies",
title = "Medical"
) +
theme(
plot.title = element_text(size = 12)
)
top_10_base_spp <- spp_moitivation_summary %>%
dplyr::filter(study_motivation == "Basic research") %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:10) %>%
dplyr::mutate(species_name = fct_reorder(species_name, n)) %>%
ggplot(aes(x=species_name, y=n)) +
geom_col(width = 0.01, colour = "#3C82C4", fill = "#3C82C4") +
geom_point(size = 2, colour = "#3C82C4", fill = "#3C82C4") +
geom_text(aes(label = n), hjust=-0.6, size=3.5, color="black") +
coord_flip() +
theme_classic() +
labs(
x = "",
y = "Number of studies",
title = "Basic"
) +
theme(
plot.title = element_text(size = 12)
)Here’s a plot to compare the top 10 spp in each study motivation more specifically
top_10_combind_plot <- grid.arrange(top_10_env_spp, top_10_med_spp, top_10_base_spp, ncol = 3)
Looking at the number of distinct species used in each motivation group
EIPAAB_database %>%
dplyr::group_by(study_motivation) %>%
dplyr::reframe(n_motivation = n_distinct(article_id),
n_distinct_spp = n_distinct(species_name))## # A tibble: 3 × 3
## study_motivation n_motivation n_distinct_spp
## <fct> <int> <int>
## 1 Environmental 510 143
## 2 Medical 234 26
## 3 Basic research 158 4313.4 Species habitat
First checking how many species have IUCN data, which we will use to assess habitat differences
species_iucn_summary <- EIPAAB_database %>%
dplyr::group_by(species_name) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::mutate(species_iucn_bin = if_else(is.na(species_iucn_doi), "No", "Yes")) %>%
dplyr::group_by(species_iucn_bin) %>%
dplyr::reframe(n = length(species_iucn_bin))
species_iucn_summary## # A tibble: 2 × 2
## species_iucn_bin n
## <chr> <int>
## 1 No 67
## 2 Yes 106Summarising the IUNC habitat type, some species will have multiple habitats, so we split the string by the sepreate (;)
habitat_summary <- EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
dplyr::group_by(species_iucn_habitat) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(species_iucn_habitat = str_trim(species_iucn_habitat)) %>%
tidyr::separate_rows(species_iucn_habitat, sep = ";") %>% # each spp has multiple habitats the string needs spliting
dplyr::group_by(species_iucn_habitat) %>%
dplyr::reframe(n_articles = sum(n)) %>% # now a sum for each habitat
arrange(desc(n_articles))
habitat_summary## # A tibble: 13 × 2
## species_iucn_habitat n_articles
## <chr> <int>
## 1 Wetlands inland 738
## 2 Artificial or Aquatic and Marine 187
## 3 <NA> 164
## 4 Marine Neritic 99
## 5 Marine Coastal or Supratidal 44
## 6 Marine Intertidal 27
## 7 Forest 23
## 8 Grassland 22
## 9 Artificial or Terrestrial 21
## 10 Shrubland 18
## 11 Savanna 13
## 12 Marine Oceanic 10
## 13 Unknown 2Checking how many freshwater vs marine species there are. Wetlands inland categories are freshwater bodies where as Marine have multiple categories (Marine Neritic, Marine Coastal or Supratidal, Marine Intertidal, Marine Oceanic).
habitat_summary %>%
dplyr::filter(str_starts(species_iucn_habitat, "Marine") | species_iucn_habitat == "Wetlands inland") %>% # Only habitats of interest
dplyr::mutate(aquatic_type = if_else(species_iucn_habitat == "Wetlands inland", "Freshwater", "Marine")) %>% # New category
dplyr::group_by(aquatic_type) %>%
dplyr::reframe(n_articles = sum(n_articles)) %>% # Final sums
dplyr::ungroup() %>%
dplyr::mutate(n_total = sum(n_articles),
percent = round(n_articles/n_total*100,1))## # A tibble: 2 × 4
## aquatic_type n_articles n_total percent
## <chr> <int> <int> <dbl>
## 1 Freshwater 738 918 80.4
## 2 Marine 180 918 19.6Lets break this up by study motivation
habitat_summary_all <- EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
dplyr::group_by(study_motivation, species_iucn_habitat) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(species_iucn_habitat = str_trim(species_iucn_habitat)) %>%
tidyr::separate_rows(species_iucn_habitat, sep = ";") %>% # each spp has multiple habitats the string needs spliting
dplyr::group_by(species_iucn_habitat, study_motivation) %>%
dplyr::reframe(n_articles = sum(n)) %>% # now a sum for each habitat
arrange(desc(n_articles))
habitat_summary_all## # A tibble: 37 × 3
## species_iucn_habitat study_motivation n_articles
## <chr> <fct> <int>
## 1 Wetlands inland Environmental 384
## 2 Wetlands inland Medical 224
## 3 Artificial or Aquatic and Marine Environmental 135
## 4 <NA> Environmental 131
## 5 Wetlands inland Basic research 130
## 6 Marine Neritic Environmental 80
## 7 Artificial or Aquatic and Marine Basic research 40
## 8 Marine Coastal or Supratidal Environmental 33
## 9 <NA> Basic research 22
## 10 Marine Intertidal Environmental 19
## # ℹ 27 more rowsSelecting only habitats of interest and allocating to Freshwater or Marine
freshwater_marine <- habitat_summary_all %>%
dplyr::filter(str_starts(species_iucn_habitat, "Marine") | species_iucn_habitat == "Wetlands inland") %>% # Only habitats of interest
dplyr::mutate(aquatic_type = if_else(species_iucn_habitat == "Wetlands inland", "Freshwater", "Marine")) %>% # New category
dplyr::group_by(aquatic_type, study_motivation) %>%
dplyr::reframe(n_articles = sum(n_articles)) %>% # Final sums
dplyr::group_by(study_motivation) %>%
dplyr::mutate(n_cat = sum(n_articles)) %>%
dplyr::ungroup() %>%
dplyr::mutate(prop = n_articles/n_cat) # A proportion of those identify as freshwater vs marine
freshwater_marine## # A tibble: 6 × 5
## aquatic_type study_motivation n_articles n_cat prop
## <chr> <fct> <int> <int> <dbl>
## 1 Freshwater Environmental 384 520 0.738
## 2 Freshwater Medical 224 237 0.945
## 3 Freshwater Basic research 130 161 0.807
## 4 Marine Environmental 136 520 0.262
## 5 Marine Medical 13 237 0.0549
## 6 Marine Basic research 31 161 0.193aquatic_type_order <- c("Freshwater", "Marine")
# Define the black and grey color theme
#color_theme <- c("#7FAB91", "#2A4A64")
# Calculate cumulative positions for text labels
freshwater_marine <- freshwater_marine %>%
dplyr::mutate(aquatic_type = factor(aquatic_type, levels = aquatic_type_order)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::arrange(desc(aquatic_type)) %>%
dplyr::mutate(cumulative_prop = cumsum(prop) - prop / 2)
# Define the black and grey color theme
color_theme <- c("#7FAB91", "#2A4A64")
# Create the plot
habitat_fig <- freshwater_marine %>%
mutate(study_motivation = fct_relevel(study_motivation, "Basic research", "Medical", "Environmental")) %>%
ggplot(aes(y = prop, x = study_motivation, fill = aquatic_type, group = aquatic_type)) +
geom_bar(stat = "identity", width = 0.9) +
geom_text(aes(label = round(prop, 2), y = cumulative_prop), color = "white", size = 3) +
scale_fill_manual(values = color_theme, name = "Habitat") +
theme_classic() +
theme(legend.position = "right") +
labs(
x = "Study motivation",
y = "Proportion of all species assigned to freshwater or marine habitat"
) +
coord_flip()
habitat_fig
setwd(figures_path)
ggsave("spp_habitat_fig.pdf", plot = habitat_fig, width = 10, height = 5)We should also consider how many records did not inculde IUCN reports and thus habitat.
Let’s make a plot that shows how many didn’t have an assigned IUNC habitat. To add to the above figure.
no_habitat <- EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
dplyr::mutate(species_iucn_bin = if_else(is.na(species_iucn_doi), "No", "Yes")) %>%
dplyr::group_by(species_iucn_bin) %>%
dplyr::reframe(n = length(species_iucn_bin))
n_total <- EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
nrow(.)
no_habitat <- no_habitat %>%
dplyr::mutate(prop = n/n_total)
no_habitat## # A tibble: 2 × 3
## species_iucn_bin n prop
## <chr> <int> <dbl>
## 1 No 163 0.174
## 2 Yes 772 0.826Here’s the plot
# Define the black and grey color theme
black_and_grey <- c("#BCBEC0", "#414042")
yes_order <- c("Yes", "No")
# Calculate cumulative positions for text labels
habitat_info_df_fig <- no_habitat %>%
dplyr::mutate(species_iucn_bin = factor(species_iucn_bin, levels = yes_order)) %>%
dplyr::arrange(desc(species_iucn_bin)) %>%
dplyr::mutate(cumulative_prop = cumsum(prop) - prop / 2)
habitat_info_fig <- habitat_info_df_fig %>%
dplyr::mutate(species_iucn_bin = factor(species_iucn_bin, levels = yes_order)) %>%
ggplot(aes(y = prop, x = 1, fill = species_iucn_bin)) +
geom_bar(stat = "identity", width = 0.1) +
geom_text(aes(label = round(prop, 2), y = cumulative_prop), color = "white") +
scale_fill_manual(values = black_and_grey, name = "Habitat") +
theme_classic() +
theme(legend.position = "right",
axis.text.x = element_blank(), # Remove x-axis text
axis.ticks.x = element_blank() # Remove x-axis ticks
) +
labs(
x = "",
y = "Proportion of species tested in the database"
)
habitat_info_fig
setwd(figures_path)
ggsave("spp_habitat_info_fig.pdf", plot = habitat_info_fig, width = 5, height = 10)13.5 Species life satge
Let’s get an overall summary first, without Unknown or not specified life stages
EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
dplyr::group_by(species_stage) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(species_stage = str_trim(species_stage)) %>%
tidyr::separate_rows(species_stage, sep = ";") %>% # each spp has multiple habitats the string needs splitting
dplyr::filter(species_stage != "Unknown or not specified") %>%
dplyr::group_by(species_stage) %>%
dplyr::reframe(n_articles = sum(n)) %>% # now a sum for each habitat
dplyr::mutate(total_stages = sum(n_articles)) %>%
dplyr::ungroup() %>%
dplyr::mutate(overall_percent = round(n_articles/total_stages*100,1))## # A tibble: 4 × 4
## species_stage n_articles total_stages overall_percent
## <chr> <int> <int> <dbl>
## 1 Adult 443 831 53.3
## 2 Egg or embryo 46 831 5.5
## 3 Juvenile 123 831 14.8
## 4 Larvae 219 831 26.4Let’s take a look at spp life stages used in the EIPAAB databasebased on study motivation
stage_summary_all <- EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
dplyr::group_by(study_motivation, species_stage) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(species_stage = str_trim(species_stage)) %>%
tidyr::separate_rows(species_stage, sep = ";") %>% # each spp has multiple habitats the string needs splitting
dplyr::group_by(species_stage, study_motivation) %>%
dplyr::reframe(n_total = sum(n)) %>% # now a sum for each habitat
dplyr::group_by(study_motivation) %>%
dplyr::mutate(n_motivation = sum(n_total)) %>%
dplyr::ungroup()
stage_summary_all## # A tibble: 15 × 4
## species_stage study_motivation n_total n_motivation
## <chr> <fct> <int> <int>
## 1 Adult Environmental 236 586
## 2 Adult Medical 134 245
## 3 Adult Basic research 73 166
## 4 Egg or embryo Environmental 31 586
## 5 Egg or embryo Medical 11 245
## 6 Egg or embryo Basic research 4 166
## 7 Juvenile Environmental 92 586
## 8 Juvenile Medical 14 245
## 9 Juvenile Basic research 17 166
## 10 Larvae Environmental 127 586
## 11 Larvae Medical 64 245
## 12 Larvae Basic research 28 166
## 13 Unknown or not specified Environmental 100 586
## 14 Unknown or not specified Medical 22 245
## 15 Unknown or not specified Basic research 44 166For life stages that are described, what is the breakdown
stage_order <- c("Adult", "Juvenile", "Larvae", "Egg or embryo")
# Define the black and grey color theme
color_theme <- c("#A14323", "#D86C2F", "#EE9E5A", "#F3E9A5")
# Making proportion for those that did report
stage_summary_described <- stage_summary_all %>%
dplyr::filter(species_stage %in% stage_order) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(n_motivation = sum(n_total)) %>%
dplyr::ungroup() %>%
dplyr::mutate(prop = n_total/n_motivation)
# Calculate cumulative positions for text labels
stage_summary_described <- stage_summary_described %>%
dplyr::mutate(species_stage = factor(species_stage, levels = stage_order)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::arrange(desc(species_stage)) %>%
dplyr::mutate(cumulative_prop = cumsum(prop) - prop / 4)
# Create the plot
life_stage_fig <- stage_summary_described %>%
mutate(study_motivation = fct_relevel(study_motivation, "Basic research", "Medical", "Environmental")) %>%
ggplot(aes(y = prop, x = study_motivation, fill = species_stage, group = species_stage)) +
geom_bar(stat = "identity", width = 0.9) +
geom_text(aes(label = round(prop, 2), y = cumulative_prop), color = "white", size = 3) +
scale_fill_manual(values = color_theme, name = "Life stage") +
theme_classic() +
theme(legend.position = "right") +
labs(
x = "Study motivation",
y = "Proportion of all species assigned to a life stage"
) +
coord_flip()
life_stage_fig
setwd(figures_path)
ggsave("spp_life_stage_fig.pdf", plot = life_stage_fig, width = 10, height = 5)Let’s also look at how many where unknown or not described
stage_summary_info <- stage_summary_all %>%
dplyr::mutate(stage_reported = if_else(species_stage == "Unknown or not specified", "No", "Yes")) %>%
dplyr::group_by(stage_reported) %>%
dplyr::reframe(n = sum(n_total))
n_total <- stage_summary_info %>%
dplyr::reframe(n_total = sum(n)) %>%
pull(n_total)
stage_summary_info <- stage_summary_info %>%
dplyr::mutate(prop = n/n_total)Here’s the plot
# Define the black and grey color theme
black_and_grey <- c("#BCBEC0", "#414042")
yes_order <- c("Yes", "No")
# Calculate cumulative positions for text labels
stage_summary_info <- stage_summary_info %>%
dplyr::mutate(stage_reported = factor(stage_reported, levels = yes_order)) %>%
dplyr::arrange(desc(stage_reported)) %>%
dplyr::mutate(cumulative_prop = cumsum(prop) - prop / 2)
stage_info_fig <- stage_summary_info %>%
dplyr::mutate(stage_reported = factor(stage_reported, levels = yes_order)) %>%
ggplot(aes(y = prop, x = 1, fill = stage_reported)) +
geom_bar(stat = "identity", width = 0.9) +
geom_text(aes(label = round(prop, 2), y = cumulative_prop), color = "white") +
scale_fill_manual(values = black_and_grey, name = "Stage reported") +
theme_classic() +
theme(legend.position = "right",
axis.text.x = element_blank(), # Remove x-axis text
axis.ticks.x = element_blank() # Remove x-axis ticks
) +
labs(
x = "",
y = "Proportion of species tested in the database"
)
stage_info_fig
setwd(figures_path)
ggsave("spp_stage_info_fig.pdf", plot = stage_info_fig, width = 2.5, height = 5)13.6 Species sex
First overall breakdown by female and male without including unreported or hermaphroditic animals
EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
dplyr::group_by(species_sex) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(species_sex = str_trim(species_sex)) %>%
tidyr::separate_rows(species_sex, sep = ";") %>% # each spp has multiple habitats the string needs splitting
dplyr::group_by(species_sex) %>%
dplyr::summarise(n_articles = sum(n), .groups = 'drop') %>% # now a sum for each habitat
tidyr::complete(species_sex,
fill = list(n_articles = 0)) %>% # make a full df with empty categories = 0
dplyr::ungroup() %>%
dplyr::filter(species_sex == "Female" | species_sex == "Male") %>%
dplyr::mutate(total_sex = sum(n_articles)) %>%
dplyr::ungroup() %>%
dplyr::mutate(overall_percent = round(n_articles/total_sex*100,1))## # A tibble: 2 × 4
## species_sex n_articles total_sex overall_percent
## <chr> <int> <int> <dbl>
## 1 Female 280 623 44.9
## 2 Male 343 623 55.1Let’s take a look at the sex of spp used in the EIPAAB database
sex_summary_all <- EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
dplyr::group_by(study_motivation, species_sex) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(species_sex = str_trim(species_sex)) %>%
tidyr::separate_rows(species_sex, sep = ";") %>% # each spp has multiple habitats the string needs splitting
dplyr::group_by(species_sex, study_motivation) %>%
dplyr::summarise(n_articles = sum(n), .groups = 'drop') %>% # now a sum for each habitat
tidyr::complete(species_sex, study_motivation,
fill = list(n_articles = 0)) %>% # make a full df with empty categories = 0
dplyr::ungroup() %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_sex = sum(n_articles)) %>%
dplyr::ungroup() %>%
dplyr::mutate(overall_prop = n_articles/total_sex)
sex_summary_all## # A tibble: 12 × 5
## species_sex study_motivation n_articles total_sex overall_prop
## <chr> <fct> <int> <int> <dbl>
## 1 Female Environmental 136 645 0.211
## 2 Female Medical 91 323 0.282
## 3 Female Basic research 53 205 0.259
## 4 Hermaphrodites Environmental 4 645 0.00620
## 5 Hermaphrodites Medical 0 323 0
## 6 Hermaphrodites Basic research 0 205 0
## 7 Male Environmental 180 645 0.279
## 8 Male Medical 100 323 0.310
## 9 Male Basic research 63 205 0.307
## 10 Unknown or not specified Environmental 325 645 0.504
## 11 Unknown or not specified Medical 132 323 0.409
## 12 Unknown or not specified Basic research 89 205 0.434Let’s look at just the proportion of those defined as male and female
sex_male_female <- sex_summary_all %>%
dplyr::filter(species_sex == "Female" | species_sex == "Male") %>%
dplyr::select(-total_sex, -overall_prop) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_male_female = sum(n_articles)) %>%
dplyr::ungroup() %>%
dplyr::mutate(prop = n_articles/total_male_female)Making the plot
sex_order <- c("Female", "Male")
color_theme <- c("#eb4729", "#1b909a")
# Calculate cumulative positions for text labels
sex_male_female <- sex_male_female %>%
dplyr::mutate(species_sex = factor(species_sex, levels = sex_order)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::arrange(desc(species_sex)) %>%
dplyr::mutate(cumulative_prop = cumsum(prop) - prop / 2)
# Create the plot
sex_fig <- sex_male_female %>%
mutate(study_motivation = fct_relevel(study_motivation, "Basic research", "Medical", "Environmental")) %>%
ggplot(aes(y = prop, x = study_motivation, fill = species_sex, group = species_sex)) +
geom_bar(stat = "identity", width = 0.9) +
geom_text(aes(label = round(prop, 2), y = cumulative_prop), color = "white", size = 3) +
scale_fill_manual(values = color_theme, name = "Sex") +
theme_classic() +
theme(legend.position = "right") +
labs(
x = "Study motivation",
y = "Proportion of all species assigned to female or male"
) +
coord_flip()
sex_fig
setwd(figures_path)
ggsave("spp_sex_fig.pdf", plot = sex_fig, width = 10, height = 5)Now let’s look at those not assgined to a sex
sex_summary_info <- sex_summary_all %>%
dplyr::mutate(sex_reported = if_else(species_sex == "Unknown or not specified", "No", "Yes")) %>%
dplyr::group_by(sex_reported) %>%
dplyr::reframe(n = sum(n_articles))
n_total <- sex_summary_info %>%
dplyr::reframe(n_total = sum(n)) %>%
dplyr::pull(n_total)
sex_summary_info <- sex_summary_info %>%
dplyr::mutate(prop = n/n_total)Here’s the plot
# Define the black and grey color theme
black_and_grey <- c("#BCBEC0", "#414042")
yes_order <- c("Yes", "No")
# Calculate cumulative positions for text labels
sex_summary_info <- sex_summary_info %>%
dplyr::mutate(sex_reported = factor(sex_reported, levels = yes_order)) %>%
dplyr::arrange(desc(sex_reported)) %>%
dplyr::mutate(cumulative_prop = cumsum(prop) - prop / 2)
sex_info_fig <- sex_summary_info %>%
dplyr::mutate(sex_reported = factor(sex_reported, levels = yes_order)) %>%
ggplot(aes(y = prop, x = 1, fill = sex_reported)) +
geom_bar(stat = "identity", width = 0.9) +
geom_text(aes(label = round(prop, 2), y = cumulative_prop), color = "white") +
scale_fill_manual(values = black_and_grey, name = "Sex reported") +
theme_classic() +
theme(legend.position = "right",
axis.text.x = element_blank(), # Remove x-axis text
axis.ticks.x = element_blank() # Remove x-axis ticks
) +
labs(
x = "",
y = "Proportion of all species"
)
sex_info_fig
setwd(figures_path)
ggsave("spp_sex_info_fig.pdf", plot = sex_info_fig, width = 2.5, height = 5)13.7 Species source
Breakdown without unreported
EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
dplyr::group_by(species_source) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(species_source = str_trim(species_source)) %>%
tidyr::separate_rows(species_source, sep = ";") %>% # each spp has multiple habitats the string needs splitting
dplyr::group_by(species_source) %>%
dplyr::summarise(n_articles = sum(n), .groups = 'drop') %>% # now a sum for each habitat
tidyr::complete(species_source,
fill = list(n_articles = 0)) %>% # make a full df with empty categories = 0
dplyr::filter(species_source != "Not reported") %>%
dplyr::ungroup() %>%
dplyr::mutate(total_source = sum(n_articles)) %>%
dplyr::ungroup() %>%
dplyr::mutate(overall_percent = round(n_articles/total_source*100, 1)) %>%
dplyr::arrange(desc(overall_percent))## # A tibble: 5 × 4
## species_source n_articles total_source overall_percent
## <chr> <int> <int> <dbl>
## 1 Commercial supplier or fish farm 305 802 38
## 2 Lab stock of undisclosed origin 213 802 26.6
## 3 Wild collected 196 802 24.4
## 4 Lab stock from commercial supplier 55 802 6.9
## 5 Lab stock from wild population 33 802 4.1Let’s look at where the animals were sourced for articles in the EIPAAB database
source_summary_all <- EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
dplyr::group_by(study_motivation, species_source) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(species_source = str_trim(species_source)) %>%
tidyr::separate_rows(species_source, sep = ";") %>% # each spp has multiple habitats the string needs splitting
dplyr::group_by(species_source, study_motivation) %>%
dplyr::summarise(n_articles = sum(n), .groups = 'drop') %>% # now a sum for each habitat
tidyr::complete(species_source, study_motivation,
fill = list(n_articles = 0)) %>% # make a full df with empty categories = 0
dplyr::ungroup() %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_source = sum(n_articles)) %>%
dplyr::ungroup() %>%
dplyr::mutate(overall_prop = n_articles/total_source)
source_summary_all## # A tibble: 18 × 5
## species_source study_motivation n_articles total_source overall_prop
## <chr> <fct> <int> <int> <dbl>
## 1 Commercial supplier or… Environmental 134 548 0.245
## 2 Commercial supplier or… Medical 101 241 0.419
## 3 Commercial supplier or… Basic research 71 161 0.441
## 4 Lab stock from commerc… Environmental 29 548 0.0529
## 5 Lab stock from commerc… Medical 16 241 0.0664
## 6 Lab stock from commerc… Basic research 10 161 0.0621
## 7 Lab stock from wild po… Environmental 25 548 0.0456
## 8 Lab stock from wild po… Medical 1 241 0.00415
## 9 Lab stock from wild po… Basic research 7 161 0.0435
## 10 Lab stock of undisclos… Environmental 119 548 0.217
## 11 Lab stock of undisclos… Medical 64 241 0.266
## 12 Lab stock of undisclos… Basic research 30 161 0.186
## 13 Not reported Environmental 72 548 0.131
## 14 Not reported Medical 51 241 0.212
## 15 Not reported Basic research 25 161 0.155
## 16 Wild collected Environmental 169 548 0.308
## 17 Wild collected Medical 8 241 0.0332
## 18 Wild collected Basic research 18 161 0.112source_summary <- source_summary_all %>%
dplyr::filter(species_source != "Not reported") %>%
dplyr::select(species_source, study_motivation, n_articles) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_reported = sum(n_articles)) %>%
dplyr::ungroup() %>%
dplyr::mutate(prop = n_articles/total_reported)
source_summary## # A tibble: 15 × 5
## species_source study_motivation n_articles total_reported prop
## <chr> <fct> <int> <int> <dbl>
## 1 Commercial supplier or fi… Environmental 134 476 0.282
## 2 Commercial supplier or fi… Medical 101 190 0.532
## 3 Commercial supplier or fi… Basic research 71 136 0.522
## 4 Lab stock from commercial… Environmental 29 476 0.0609
## 5 Lab stock from commercial… Medical 16 190 0.0842
## 6 Lab stock from commercial… Basic research 10 136 0.0735
## 7 Lab stock from wild popul… Environmental 25 476 0.0525
## 8 Lab stock from wild popul… Medical 1 190 0.00526
## 9 Lab stock from wild popul… Basic research 7 136 0.0515
## 10 Lab stock of undisclosed … Environmental 119 476 0.25
## 11 Lab stock of undisclosed … Medical 64 190 0.337
## 12 Lab stock of undisclosed … Basic research 30 136 0.221
## 13 Wild collected Environmental 169 476 0.355
## 14 Wild collected Medical 8 190 0.0421
## 15 Wild collected Basic research 18 136 0.132source_order <- c("Wild collected", "Lab stock from wild population", "Lab stock of undisclosed origin",
"Lab stock from commercial supplier", "Commercial supplier or fish farm")
# Define the black and grey color theme
color_theme <- c("#607C3B", "#A7D271", "#6B6E70", "#A66EAF", "#61346B")
# Calculate cumulative positions for text labels
source_summary <- source_summary %>%
dplyr::mutate(species_source = factor(species_source, levels = source_order)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::arrange(desc(species_source)) %>%
dplyr::mutate(cumulative_prop = cumsum(prop) - prop / 5)
# Create the plot
source_fig <- source_summary %>%
mutate(study_motivation = fct_relevel(study_motivation, "Basic research", "Medical", "Environmental")) %>%
ggplot(aes(y = prop, x = study_motivation, fill = species_source, group = species_source)) +
geom_bar(stat = "identity", width = 0.9) +
geom_text(aes(label = round(prop, 2), y = cumulative_prop), color = "white", size = 3) +
scale_fill_manual(values = color_theme, name = "Life stage") +
theme_classic() +
theme(legend.position = "right") +
labs(
x = "Study motivation",
y = "Proportion of all species with a described source"
) +
coord_flip()
source_fig
setwd(figures_path)
ggsave("spp_source_fig.pdf", plot = source_fig, width = 10, height = 5)Now let’s look at those not assigned a source
source_summary_info <- source_summary_all %>%
dplyr::mutate(source_reported = if_else(species_source == "Not reported", "No", "Yes")) %>%
dplyr::group_by(source_reported) %>%
dplyr::reframe(n = sum(n_articles))
n_total <- source_summary_info %>%
dplyr::reframe(n_total = sum(n)) %>%
dplyr::pull(n_total)
source_summary_info <- source_summary_info %>%
dplyr::mutate(prop = n/n_total)Here’s the plot
# Define the black and grey color theme
black_and_grey <- c("#BCBEC0", "#414042")
yes_order <- c("Yes", "No")
# Calculate cumulative positions for text labels
source_summary_info <- source_summary_info %>%
dplyr::mutate(source_reported = factor(source_reported, levels = yes_order)) %>%
dplyr::arrange(desc(source_reported)) %>%
dplyr::mutate(cumulative_prop = cumsum(prop) - prop / 2)
source_info_fig <- source_summary_info %>%
dplyr::mutate(source_reported = factor(source_reported, levels = yes_order)) %>%
ggplot(aes(y = prop, x = 1, fill = source_reported)) +
geom_bar(stat = "identity", width = 0.9) +
geom_text(aes(label = round(prop, 2), y = cumulative_prop), color = "white") +
scale_fill_manual(values = black_and_grey, name = "Source reported") +
theme_classic() +
theme(legend.position = "right",
axis.text.x = element_blank(), # Remove x-axis text
axis.ticks.x = element_blank() # Remove x-axis ticks
) +
labs(
x = "",
y = "Proportion of all species"
)
source_info_fig
setwd(figures_path)
ggsave("spp_source_info_fig.pdf", plot = source_info_fig, width = 5, height = 10)14 Compounds
14.1 Number of compounds in database
There are 426 distinct compounds in the database
EIPAAB_database %>%
dplyr::distinct(compound_name) %>%
nrow()## [1] 42614.1.1 Number of compounds per study
article_n <- EIPAAB_database %>%
dplyr::distinct(article_id) %>%
nrow(.)
compound_n_summary <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(compound_n) %>%
dplyr::reframe(n = n(),
percent = round((n/article_n)*100,1))
compound_n_summary## # A tibble: 18 × 3
## compound_n n percent
## <int> <int> <dbl>
## 1 1 624 69.3
## 2 2 127 14.1
## 3 3 67 7.4
## 4 4 32 3.6
## 5 5 16 1.8
## 6 6 8 0.9
## 7 7 6 0.7
## 8 8 5 0.6
## 9 9 1 0.1
## 10 10 2 0.2
## 11 11 3 0.3
## 12 12 2 0.2
## 13 13 2 0.2
## 14 14 2 0.2
## 15 16 1 0.1
## 16 18 1 0.1
## 17 25 1 0.1
## 18 52 1 0.1How mnay used more then 5
compound_n_summary %>%
dplyr::filter(compound_n > 5) %>%
reframe(n = sum(n),
percent = round((n/article_n)*100,1))## # A tibble: 1 × 2
## n percent
## <int> <dbl>
## 1 35 3.9compound_n_summary <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(compound_n, study_motivation) %>%
dplyr::summarise(n = n(), .groups = 'drop') %>%
tidyr::complete(compound_n, study_motivation, fill = list(n = 0)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(prop_motivation = n/total_motivation)
compound_n_summary## # A tibble: 54 × 5
## compound_n study_motivation n total_motivation prop_motivation
## <int> <fct> <int> <int> <dbl>
## 1 1 Environmental 389 510 0.763
## 2 1 Medical 135 234 0.577
## 3 1 Basic research 100 157 0.637
## 4 2 Environmental 61 510 0.120
## 5 2 Medical 42 234 0.179
## 6 2 Basic research 24 157 0.153
## 7 3 Environmental 27 510 0.0529
## 8 3 Medical 26 234 0.111
## 9 3 Basic research 14 157 0.0892
## 10 4 Environmental 17 510 0.0333
## # ℹ 44 more rows# Define the colour palette
motivation_colour_theme <- c("#60BD6C", "#D359A1", "#3C82C4") # Making colour theme to apply to plot
compound_n_oder <- c(1:9, ">10")
compound_n_fig <- compound_n_summary %>%
dplyr::mutate(
compound_n = as.character(if_else(compound_n > 10, 10, compound_n)), # Grouping cases above 10
compound_n = if_else(compound_n == "10", ">10", compound_n)
) %>%
dplyr::group_by(compound_n, study_motivation) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(compound_n = factor(compound_n, levels = compound_n_oder)) %>%
ggplot(aes(x=compound_n, y=n, colour = study_motivation, fill = study_motivation, group = study_motivation)) +
geom_col(position = position_dodge(width = 0.8), width = 0.1) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_text(aes(label = n), vjust=-0.6, size=3.5, color="black", position = position_dodge(width = 0.8)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
labs(
x = "",
y = "Number of studies"
) +
theme()
compound_n_fig
setwd(figures_path)
ggsave("comp_compound_n_fig.pdf", plot = compound_n_fig, width = 10, height = 5)14.2 Therputic diversity in the database
First lets see how many compounds have an ATC classification.
305 out of 426 (71.6%)
EIPAAB_database %>%
dplyr::group_by(compound_name) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
dplyr::group_by(compound_atc_boolean) %>%
dplyr::reframe(n = length(compound_atc_boolean))## # A tibble: 2 × 2
## compound_atc_boolean n
## <chr> <int>
## 1 No 121
## 2 Yes 30514.2.1 ATC level 1
Let’s seem how many classes there are at the Anatomical Therapeutic Chemical (ATC) level 1
There are 14 classes at the 1st ATC level (the highest class of the ATC). This is ever class at the first level
n_compound_atc <- EIPAAB_database %>%
dplyr::filter(compound_atc_boolean == "Yes") %>%
dplyr::distinct(compound_name) %>%
nrow(.)
compound_ATC_L1_summary <- EIPAAB_database %>%
dplyr::group_by(compound_name) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
dplyr::filter(compound_atc_boolean == "Yes") %>%
dplyr::group_by(compound_atc_level_1) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(compound_atc_level_1 = str_trim(compound_atc_level_1)) %>%
tidyr::separate_rows(compound_atc_level_1, sep = ";") %>% # each spp has multiple habitats the string needs splitting
dplyr::group_by(compound_atc_level_1) %>%
dplyr::reframe(n = sum(n),
percent = round(n/n_compound_atc*100,1),
measure = "compounds") %>% # now a sum for each habitat
arrange(desc(n))
compound_ATC_L1_summary## # A tibble: 14 × 4
## compound_atc_level_1 n percent measure
## <chr> <int> <dbl> <chr>
## 1 n nervous system 137 44.9 compou…
## 2 c cardiovascular system 49 16.1 compou…
## 3 a alimentary tract and metabolism 35 11.5 compou…
## 4 s sensory organs 34 11.1 compou…
## 5 g genito urinary system and sex hormones 30 9.8 compou…
## 6 j antiinfectives for systemic use 28 9.2 compou…
## 7 d dermatologicals 27 8.9 compou…
## 8 r respiratory system 26 8.5 compou…
## 9 l antineoplastic and immunomodulating agents 19 6.2 compou…
## 10 m musculo-skeletal system 12 3.9 compou…
## 11 v various 9 3 compou…
## 12 h systemic hormonal preparations, excl. sex hormones a… 8 2.6 compou…
## 13 p antiparasitic products, insecticides and repellents 6 2 compou…
## 14 b blood and blood forming organs 4 1.3 compou…Now we will make a similar data file to look at the overall use in the database at each ATC level 1
n_data_atc <- EIPAAB_database %>%
dplyr::filter(compound_atc_boolean == "Yes") %>%
nrow(.)
compound_ATC_L1_data_summary <- EIPAAB_database %>%
dplyr::filter(compound_atc_boolean == "Yes") %>%
dplyr::group_by(compound_atc_level_1) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(compound_atc_level_1 = str_trim(compound_atc_level_1)) %>%
tidyr::separate_rows(compound_atc_level_1, sep = ";") %>% # each spp has multiple habitats the string needs splitting
dplyr::group_by(compound_atc_level_1) %>%
dplyr::reframe(n = sum(n),
percent =round(n/n_data_atc*100,1),
measure = "data") %>% # now a sum for each habitat
arrange(desc(percent))
compound_ATC_L1_data_summary## # A tibble: 14 × 4
## compound_atc_level_1 n percent measure
## <chr> <int> <dbl> <chr>
## 1 n nervous system 1120 72.9 data
## 2 g genito urinary system and sex hormones 201 13.1 data
## 3 c cardiovascular system 158 10.3 data
## 4 d dermatologicals 130 8.5 data
## 5 s sensory organs 102 6.6 data
## 6 a alimentary tract and metabolism 93 6.1 data
## 7 l antineoplastic and immunomodulating agents 89 5.8 data
## 8 r respiratory system 76 4.9 data
## 9 m musculo-skeletal system 58 3.8 data
## 10 j antiinfectives for systemic use 57 3.7 data
## 11 v various 32 2.1 data
## 12 h systemic hormonal preparations, excl. sex hormones a… 15 1 data
## 13 b blood and blood forming organs 12 0.8 data
## 14 p antiparasitic products, insecticides and repellents 7 0.5 dataATC_L1_summary <- compound_ATC_L1_summary %>%
rbind(., compound_ATC_L1_data_summary) %>%
dplyr::mutate(value = if_else(measure == "compounds", n, percent))This plot shows the number of different compounds in each ATC classification as well as the total proportion of data it makes up
measure_colour_theme <- c("black", "grey") # Making colour theme to apply to plot
# Making a list of act names in the order that we want them in the plot
level_1_order <- ATC_L1_summary %>%
dplyr::filter(measure == "data") %>%
dplyr::arrange(value) %>%
dplyr::pull(compound_atc_level_1)
# Making the plot
atc_level_1_fig <- ATC_L1_summary %>%
dplyr::mutate(compound_atc_level_1 = factor(compound_atc_level_1, levels = level_1_order)) %>%
ggplot(aes(x=compound_atc_level_1, y=value, colour = measure, fill = measure, group = measure)) +
geom_col(position = position_dodge(width = 0.8), width = 0.2, colour = NA) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_text(aes(label = value), hjust=-0.6, size=3.5, color="black", position = position_dodge(width = 0.8)) +
scale_colour_manual(values = measure_colour_theme, name = "Value type") +
scale_fill_manual(values = measure_colour_theme, name = "Value type") +
coord_flip() +
scale_y_continuous(
name = "Number of distinct compounds",
sec.axis = sec_axis(~ . , name = "Total proportion of the database") # Adjust scaling if needed
) +
theme_classic() +
labs(
x = "",
y = "Number of distict species in the database"
) +
theme()
atc_level_1_fig
setwd(figures_path)
ggsave("comp_atc_level_1_fig.pdf", plot = atc_level_1_fig, width = 10, height = 10)14.2.2 ATC level 3
Let’s seem how many classes there are at the Anatomical Therapeutic Chemical (ATC) level 3
There are 131 distinct classes
n_compound_atc <- EIPAAB_database %>%
dplyr::filter(compound_atc_boolean == "Yes") %>%
dplyr::distinct(compound_name) %>%
nrow(.)
compound_ATC_L3_summary <- EIPAAB_database %>%
dplyr::group_by(compound_name) %>%
dplyr::sample_n(1) %>% # sampling one row per article per species (i.e. ignoring multiple rows per article for compounds)
dplyr::ungroup() %>%
dplyr::filter(compound_atc_boolean == "Yes") %>%
dplyr::group_by(compound_atc_level_3) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(compound_atc_level_3 = str_trim(compound_atc_level_3)) %>%
tidyr::separate_rows(compound_atc_level_3, sep = ";") %>% # each spp has multiple habitats the string needs splitting
dplyr::group_by(compound_atc_level_3) %>%
dplyr::reframe(n = sum(n),
percent = round(n/n_compound_atc*100,1),
measure = "compounds") %>% # now a sum for each habitat
arrange(desc(n))
compound_ATC_L3_summary## # A tibble: 131 × 4
## compound_atc_level_3 n percent measure
## <chr> <int> <dbl> <chr>
## 1 n06a antidepressants 27 8.9 compou…
## 2 n03a antiepileptics 18 5.9 compou…
## 3 n05a antipsychotics 14 4.6 compou…
## 4 a01a stomatological preparations 12 3.9 compou…
## 5 n05b anxiolytics 11 3.6 compou…
## 6 n05c hypnotics and sedatives 11 3.6 compou…
## 7 n06b psychostimulants, agents used for adhd and nootro… 11 3.6 compou…
## 8 r06a antihistamines for systemic use 11 3.6 compou…
## 9 c07a beta blocking agents 9 3 compou…
## 10 d04a antipruritics, incl. antihistamines, anesthetics,… 9 3 compou…
## # ℹ 121 more rowsNow we will make a similar data file to look at the overall use in the database at each ATC level 3
n_data_atc <- EIPAAB_database %>%
dplyr::filter(compound_atc_boolean == "Yes") %>%
nrow(.)
compound_ATC_L3_data_summary <- EIPAAB_database %>%
dplyr::filter(compound_atc_boolean == "Yes") %>%
dplyr::group_by(compound_atc_level_3) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(compound_atc_level_3 = str_trim(compound_atc_level_3)) %>%
tidyr::separate_rows(compound_atc_level_3, sep = ";") %>% # each spp has multiple habitats the string needs splitting
dplyr::group_by(compound_atc_level_3) %>%
dplyr::reframe(n = sum(n),
percent = round(n/n_data_atc*100,1),
measure = "data") %>% # now a sum for each habitat
arrange(desc(percent))
compound_ATC_L3_data_summary## # A tibble: 131 × 4
## compound_atc_level_3 n percent measure
## <chr> <int> <dbl> <chr>
## 1 n06a antidepressants 425 27.7 data
## 2 n03a antiepileptics 164 10.7 data
## 3 n05b anxiolytics 149 9.7 data
## 4 g03c estrogens 121 7.9 data
## 5 n06b psychostimulants, agents used for adhd and nootro… 84 5.5 data
## 6 l02a hormones and related agents 64 4.2 data
## 7 d11a other dermatological preparations 62 4 data
## 8 n05a antipsychotics 60 3.9 data
## 9 m01a antiinflammatory and antirheumatic products, non-… 50 3.3 data
## 10 n02a opioids 51 3.3 data
## # ℹ 121 more rowsHere we make a new column called value where we combined the count of distinct compounds and proportion of data
ATC_L3_summary <- compound_ATC_L3_summary %>%
rbind(., compound_ATC_L3_data_summary) %>%
dplyr::mutate(value = if_else(measure == "compounds", n, percent))This plot shows the number of different compounds in each ATC classification as well as the total proportion of data it makes up. This is done for only the 15 most commonly used groups.
measure_colour_theme <- c("black", "grey") # Making colour theme to apply to plot
# Making a list of act names in the order that we want them in the plot
level_3_order_top_15 <- ATC_L3_summary %>%
dplyr::filter(measure == "data") %>%
dplyr::arrange(desc(value)) %>%
dplyr::slice(1:15) %>%
dplyr::arrange(desc(value)) %>%
dplyr::pull(compound_atc_level_3)
# Making the plot
atc_level_3_fig <- ATC_L3_summary %>%
dplyr::filter(compound_atc_level_3 %in% level_3_order_top_15) %>%
dplyr::mutate(compound_atc_level_3 = factor(compound_atc_level_3, levels = level_3_order_top_15)) %>%
ggplot(aes(x=compound_atc_level_3, y=value, fill = measure, colour = measure,
group = measure)) +
geom_col(position = position_dodge(width = 1), width = 0.2, colour = NA,) +
geom_point(position = position_dodge(width = 1), size = 3) +
geom_text(aes(label = value), vjust=-0.6, size=3.5, position = position_dodge(width = 1)) +
scale_colour_manual(values = measure_colour_theme, name = "Value type",
labels = c("Compounds (n)", "Percentage of data")) +
scale_fill_manual(values = measure_colour_theme, name = "Value type",
labels = c("Compounds (n)", "Percentage of data")) +
scale_y_continuous(
name = "Distinct compounds",
limits = c(0, 30), # Set the range of y-axis
breaks = c(0, 10, 20, 30), # Set the labels at 0, 10, 20, 30
sec.axis = sec_axis(~ . , name = "Percentage of database") # Adjust scaling if needed
) +
theme_classic() +
labs(
x = "",
y = ""
) +
theme(
axis.text.y = element_text(size = 8), # Change y-axis labels size
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size = 8), # Change x-axis labels orientation
legend.title = element_text(size = 12), # Change legend title size if needed
legend.text = element_text(size = 10)
)
atc_level_3_fig
setwd(figures_path)
ggsave("atc_level_3_fig.pdf", plot = atc_level_3_fig, width = 10, height = 5)14.3 Most common compounds
Overall the most common compounds are Fluoxetine, Diazepam and 17-alpha-ethinylestradiol
n_row <- EIPAAB_database %>%
nrow(.)
compound_use <- EIPAAB_database %>%
dplyr::group_by(compound_name) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(prop = n/n_row) %>%
arrange(desc(prop))
compound_use## # A tibble: 426 × 3
## compound_name n prop
## <chr> <int> <dbl>
## 1 Fluoxetine 201 0.116
## 2 Diazepam 67 0.0385
## 3 17-alpha-ethinylestradiol 63 0.0362
## 4 Caffeine 45 0.0259
## 5 Venlafaxine 43 0.0247
## 6 Citalopram 42 0.0241
## 7 Sertraline 39 0.0224
## 8 Carbamazepine 38 0.0218
## 9 Buspirone 30 0.0172
## 10 Morphine 27 0.0155
## # ℹ 416 more rowsLet’s see what the numbers are for each motivation, but let’s also maintain the overall numbers so we can add it to the figure
n_row <- EIPAAB_database %>%
nrow(.)
compound_use_motivation <- EIPAAB_database %>%
dplyr::group_by(study_motivation, compound_name) %>%
dplyr::summarise(n = n(), .groups = 'drop') %>%
tidyr::complete(compound_name, study_motivation, fill = list(n = 0)) # Making sure we have a complete dataframe
compound_use_motivation <- compound_use %>%
dplyr::select(-prop) %>%
dplyr::mutate(study_motivation = "All") %>%
rbind(., compound_use_motivation)
compound_use_motivation## # A tibble: 1,704 × 3
## compound_name n study_motivation
## <chr> <int> <chr>
## 1 Fluoxetine 201 All
## 2 Diazepam 67 All
## 3 17-alpha-ethinylestradiol 63 All
## 4 Caffeine 45 All
## 5 Venlafaxine 43 All
## 6 Citalopram 42 All
## 7 Sertraline 39 All
## 8 Carbamazepine 38 All
## 9 Buspirone 30 All
## 10 Morphine 27 All
## # ℹ 1,694 more rowsThe top 10 based on each study motivation as well as the overall total
top_10_comp_all_fig <- compound_use_motivation %>%
dplyr::filter(study_motivation == "All") %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:10) %>%
dplyr::mutate(compound_name = fct_reorder(compound_name, n)) %>%
ggplot(aes(x=compound_name, y=n)) +
geom_col(width = 0.1, colour = NA, fill = "grey") +
geom_point(size = 3, colour = "grey", fill = "grey") +
geom_text(aes(label = n), hjust=-0.6, size=3.5, color="black") +
coord_flip() +
theme_classic() +
labs(
title = "All",
x = "",
y = "Total use in the database"
) +
theme(
plot.title = element_text(size = 11)
)
top_10_comp_env_fig <- compound_use_motivation %>%
dplyr::filter(study_motivation == "Environmental") %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:10) %>%
dplyr::mutate(compound_name = fct_reorder(compound_name, n)) %>%
ggplot(aes(x=compound_name, y=n)) +
geom_col(width = 0.1, colour = NA, fill = "#60BD6C") +
geom_point(size = 3, colour = "#60BD6C", fill = "#60BD6C") +
geom_text(aes(label = n), hjust=-0.6, size=3.5, color="black") +
coord_flip() +
theme_classic() +
labs(
title = "Environmental",
x = "",
y = "Total use in the database"
) +
theme(
plot.title = element_text(size = 11)
)
top_10_comp_med_fig <- compound_use_motivation %>%
dplyr::filter(study_motivation == "Medical") %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:10) %>%
dplyr::mutate(compound_name = fct_reorder(compound_name, n)) %>%
ggplot(aes(x=compound_name, y=n)) +
geom_col(width = 0.1, colour = NA, fill = "#D359A1") +
geom_point(size = 3, colour = "#D359A1", fill = "#D359A1") +
geom_text(aes(label = n), hjust=-0.6, size=3.5, color="black") +
coord_flip() +
theme_classic() +
labs(
title = "Medical",
x = "",
y = "Total use in the database"
) +
theme(
plot.title = element_text(size = 11)
)
top_10_comp_base_fig <- compound_use_motivation %>%
dplyr::filter(study_motivation == "Basic research") %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:10) %>%
dplyr::mutate(compound_name = fct_reorder(compound_name, n)) %>%
ggplot(aes(x=compound_name, y=n)) +
geom_col(width = 0.1, colour = NA, fill = "#3C82C4") +
geom_point(size = 3, colour = "#3C82C4", fill = "#3C82C4") +
geom_text(aes(label = n), hjust=-0.6, size=3.5, color="black") +
coord_flip() +
theme_classic() +
labs(
title = "Basic Research",
x = "",
y = "Total use in the database"
) +
theme(
plot.title = element_text(size = 11)
)Here are the resulting figures
top_10_comp_all_fig
top_10_comp_env_fig
top_10_comp_med_fig
top_10_comp_base_fig
Saving as PDFs
setwd(figures_path)
ggsave("comp_top_10_comp_all_fig.pdf", plot = top_10_comp_all_fig, width = 5, height = 10)
ggsave("comp_top_10_comp_env_fig.pdf", plot = top_10_comp_env_fig, width = 5, height = 10)
ggsave("comp_top_10_comp_med_fig.pdf", plot = top_10_comp_med_fig, width = 5, height = 10)
ggsave("comp_top_10_comp_base_fig.pdf", plot = top_10_comp_base_fig, width = 5, height = 10)compound_use_motivation %>%
dplyr::filter(compound_name == "17-alpha-ethinylestradiol")## # A tibble: 4 × 3
## compound_name n study_motivation
## <chr> <int> <chr>
## 1 17-alpha-ethinylestradiol 63 All
## 2 17-alpha-ethinylestradiol 61 Environmental
## 3 17-alpha-ethinylestradiol 0 Medical
## 4 17-alpha-ethinylestradiol 2 Basic research14.4 Mixtures
It was recorded whether the animals were also exposed to compound mixtures
EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::reframe(mixture_yes = sum(compond_mixture == "Yes", na.rm = TRUE),
mixture_no = sum(compond_mixture == "No", na.rm = TRUE),
mixture_percent = (mixture_yes/mixture_no)*100
)## # A tibble: 1 × 3
## mixture_yes mixture_no mixture_percent
## <int> <int> <dbl>
## 1 165 736 22.4Medical articles have a much higher use of mixtures
EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(study_motivation) %>%
dplyr::reframe(mixture_yes = sum(compond_mixture == "Yes", na.rm = TRUE),
mixture_no = sum(compond_mixture == "No", na.rm = TRUE),
mixture_percent = round((mixture_yes/mixture_no)*100,1)
)## # A tibble: 3 × 4
## study_motivation mixture_yes mixture_no mixture_percent
## <fct> <int> <int> <dbl>
## 1 Environmental 57 453 12.6
## 2 Medical 76 158 48.1
## 3 Basic research 32 125 25.614.6 Exposure route
Data on the method of exposure was also extracted
nrow <- EIPAAB_database %>%
nrow(.)
EIPAAB_database %>%
dplyr::group_by(compound_expose_route) %>%
dplyr::reframe(n = n(),
percent = round((n/nrow)*100,1)
)## # A tibble: 3 × 3
## compound_expose_route n percent
## <chr> <int> <dbl>
## 1 Other exposure route 223 12.8
## 2 Waterborne only 1501 86.3
## 3 Waterborne plus any other route 16 0.914.7 Exposure length
The database has both the minimum and maximum duration of exposure prior to behavioural measure (compound_min_duration_exposure and compound_max_duration_exposure). Here we will focus on the maximum duration
These are the different categories of exposure length
EIPAAB_database %>%
dplyr::distinct(compound_max_duration_exposure)## compound_max_duration_exposure
## 1 Less than 6 hours
## 2 1 to 3 months
## 3 3 to 8 days
## 4 22 to 29 days
## 5 Multigenerational
## 6 6 to 24 hours
## 7 1 to 3 days
## 8 8 to 15 days
## 9 Not stated
## 10 15 to 22 days
## 11 Transgenerational
## 12 3 to 6 months
## 13 LifetimeSome articles did not report the exposure duration at all, or in sufficient detail to extract.
In total this occurred in 108 cases
EIPAAB_database %>%
dplyr::filter(compound_min_duration_exposure == "Not stated" | compound_max_duration_exposure == "Not stated") %>%
nrow(.)## [1] 108exposure_duration_order <- c("Less than 6 hours", "6 to 24 hours", "1 to 3 days", "3 to 8 days", "8 to 15 days", "15 to 22 days", "22 to 29 days", "1 to 3 months", "3 to 6 months", "Lifetime", "Transgenerational", "Multigenerational")
nrow <- EIPAAB_database %>%
dplyr::filter(compound_max_duration_exposure != "Not stated") %>%
nrow(.)
exposure_duration_summary <- EIPAAB_database %>%
dplyr::filter(compound_max_duration_exposure != "Not stated") %>%
dplyr::group_by(compound_max_duration_exposure) %>%
dplyr::reframe(n = n(),
percent = round((n/nrow)*100,1)
) %>%
dplyr::mutate(compound_max_duration_exposure = factor(compound_max_duration_exposure,
levels = exposure_duration_order)) %>%
dplyr::arrange(compound_max_duration_exposure) %>%
dplyr::mutate(study_motivation = "All")
exposure_duration_summary## # A tibble: 12 × 4
## compound_max_duration_exposure n percent study_motivation
## <fct> <int> <dbl> <chr>
## 1 Less than 6 hours 679 41.3 All
## 2 6 to 24 hours 129 7.8 All
## 3 1 to 3 days 106 6.4 All
## 4 3 to 8 days 318 19.3 All
## 5 8 to 15 days 101 6.1 All
## 6 15 to 22 days 113 6.9 All
## 7 22 to 29 days 59 3.6 All
## 8 1 to 3 months 83 5 All
## 9 3 to 6 months 23 1.4 All
## 10 Lifetime 9 0.5 All
## 11 Transgenerational 15 0.9 All
## 12 Multigenerational 9 0.5 Allexposure_duration_order <- c("Less than 6 hours", "6 to 24 hours", "1 to 3 days", "3 to 8 days", "8 to 15 days", "15 to 22 days", "22 to 29 days", "1 to 3 months", "3 to 6 months", "Lifetime", "Transgenerational", "Multigenerational")
exposure_duration_motivation_summary <- EIPAAB_database %>%
dplyr::filter(compound_max_duration_exposure != "Not stated") %>%
dplyr::group_by(compound_max_duration_exposure, study_motivation) %>%
dplyr::summarise(n = n(), .groups = 'drop') %>%
tidyr::complete(compound_max_duration_exposure, study_motivation, fill = list(n = 0)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round((n/total_motivation)*100,1)) %>%
dplyr::select(-total_motivation)
exp_duration_motivation_summary <- exposure_duration_motivation_summary %>%
rbind(exposure_duration_summary) %>%
dplyr::mutate(compound_max_duration_exposure = factor(compound_max_duration_exposure,
levels = rev(exposure_duration_order))
) %>%
dplyr::arrange(desc(compound_max_duration_exposure))
exp_duration_motivation_summary## # A tibble: 48 × 4
## compound_max_duration_exposure study_motivation n percent
## <fct> <fct> <int> <dbl>
## 1 Less than 6 hours Environmental 124 15
## 2 Less than 6 hours Medical 285 61.2
## 3 Less than 6 hours Basic research 270 76.7
## 4 Less than 6 hours All 679 41.3
## 5 6 to 24 hours Environmental 62 7.5
## 6 6 to 24 hours Medical 53 11.4
## 7 6 to 24 hours Basic research 14 4
## 8 6 to 24 hours All 129 7.8
## 9 1 to 3 days Environmental 74 9
## 10 1 to 3 days Medical 22 4.7
## # ℹ 38 more rowsexp_duration_all_fig <- exp_duration_motivation_summary %>%
dplyr::filter(study_motivation == "All") %>%
ggplot(aes(x=compound_max_duration_exposure, y=percent)) +
geom_col(width = 0.1, colour = NA, fill = "grey") +
geom_point(size = 3, colour = "grey", fill = "grey") +
geom_text(aes(label = percent), vjust=-0.6, size=3.5, color="black") +
theme_classic() +
coord_flip() +
labs(
title = "All",
x = "",
y = "Total percentage"
) +
theme(
plot.title = element_text(size = 11)
)
exp_duration_env_fig <- exp_duration_motivation_summary %>%
dplyr::filter(study_motivation == "Environmental") %>%
ggplot(aes(x=compound_max_duration_exposure, y=percent)) +
geom_col(width = 0.1, colour = NA, fill = "#60BD6C") +
geom_point(size = 3, colour = "#60BD6C", fill = "#60BD6C") +
geom_text(aes(label = percent), vjust=-0.6, size=3.5, color="black") +
theme_classic() +
coord_flip() +
labs(
title = "Environmental",
x = "",
y = "Total percentage"
) +
theme(
plot.title = element_text(size = 11)
)
exp_duration_med_fig <- exp_duration_motivation_summary %>%
dplyr::filter(study_motivation == "Medical") %>%
ggplot(aes(x=compound_max_duration_exposure, y=percent)) +
geom_col(width = 0.1, colour = NA, fill = "#D359A1") +
geom_point(size = 3, colour = "#D359A1", fill = "#D359A1") +
geom_text(aes(label = percent), vjust=-0.6, size=3.5, color="black") +
theme_classic() +
coord_flip() +
labs(
title = "Medical",
x = "",
y = "Total percentage"
) +
theme(
plot.title = element_text(size = 11)
)
exp_duration_base_fig <- exp_duration_motivation_summary %>%
dplyr::filter(study_motivation == "Basic research") %>%
ggplot(aes(x=compound_max_duration_exposure, y=percent)) +
geom_col(width = 0.1, colour = NA, fill = "#3C82C4") +
geom_point(size = 3, colour = "#3C82C4", fill = "#3C82C4") +
geom_text(aes(label = percent), vjust=-0.6, size=3.5, color="black") +
theme_classic() +
coord_flip() +
labs(
title = "Basic research",
x = "",
y = "Total percentage"
) +
theme(
plot.title = element_text(size = 11)
)exp_duration_all_fig
exp_duration_env_fig
exp_duration_med_fig
exp_duration_base_fig
setwd(figures_path)
ggsave("comp_exp_duration_all_fig.pdf", plot = exp_duration_all_fig, width = 8.3/3, height = 11.7/3)
ggsave("comp_exp_duration_env_fig.pdf", plot = exp_duration_env_fig, width = 8.3/3, height = 11.7/3)
ggsave("comp_exp_duration_med_fig.pdf", plot = exp_duration_med_fig, width = 8.3/3, height = 11.7/3)
ggsave("comp_exp_duration_base_fig.pdf", plot = exp_duration_base_fig, width = 8.3/3, height = 11.7/3)14.8 Number of doses
Here I will look at the number of doses used. This was meassured as the total treatments (i.e. inculding control), so if we want to know the number of doses for the compound we need to subtract 1. I have done this below.
n_doses_summary <- EIPAAB_database %>%
dplyr::filter(!is.na(compound_treatment_levels)) %>%
dplyr::mutate(n_doses = compound_treatment_levels-1) %>%
dplyr::group_by(n_doses) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(total = sum(n),
percent = round((n/total)*100,1),
study_motivation = "All")
n_doses_summary## # A tibble: 14 × 5
## n_doses n total percent study_motivation
## <dbl> <int> <int> <dbl> <chr>
## 1 1 450 1514 29.7 All
## 2 2 203 1514 13.4 All
## 3 3 377 1514 24.9 All
## 4 4 172 1514 11.4 All
## 5 5 163 1514 10.8 All
## 6 6 46 1514 3 All
## 7 7 50 1514 3.3 All
## 8 8 14 1514 0.9 All
## 9 9 11 1514 0.7 All
## 10 10 10 1514 0.7 All
## 11 11 10 1514 0.7 All
## 12 12 5 1514 0.3 All
## 13 13 2 1514 0.1 All
## 14 17 1 1514 0.1 AllLet’s see how many use more then 5
n_doses_summary %>%
dplyr::filter(n_doses > 5) %>%
dplyr::summarise(over_5_percent = sum(percent))## # A tibble: 1 × 1
## over_5_percent
## <dbl>
## 1 9.8Looking by study motivation
n_doses_motivation_summary <- EIPAAB_database %>%
dplyr::filter(!is.na(compound_treatment_levels)) %>%
dplyr::mutate(n_doses = compound_treatment_levels-1) %>%
dplyr::group_by(n_doses, study_motivation) %>%
dplyr::summarise(n = n(), .groups = 'drop') %>%
tidyr::complete(n_doses, study_motivation, fill = list(n = 0)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round((n/total)*100,1))
n_doses_motivation_summary## # A tibble: 42 × 5
## n_doses study_motivation n total percent
## <dbl> <fct> <int> <int> <dbl>
## 1 1 Environmental 175 824 21.2
## 2 1 Medical 173 397 43.6
## 3 1 Basic research 102 293 34.8
## 4 2 Environmental 142 824 17.2
## 5 2 Medical 31 397 7.8
## 6 2 Basic research 30 293 10.2
## 7 3 Environmental 184 824 22.3
## 8 3 Medical 88 397 22.2
## 9 3 Basic research 105 293 35.8
## 10 4 Environmental 112 824 13.6
## # ℹ 32 more rowsMaking a plot
dose_order <- c(1:12, ">12")
doses_fig <- n_doses_motivation_summary %>%
dplyr::mutate(n_doses = as.character(if_else(n_doses>13, 13, n_doses)),
n_doses = if_else(n_doses == "13", ">12", n_doses),
n_doses = factor(n_doses, levels = dose_order)
)%>%
ggplot(aes(x=n_doses, y=percent, colour = study_motivation,
fill = study_motivation, group = study_motivation)) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_line(size = 1) +
geom_text(aes(label = percent), vjust=-0.6, size=3.5, color="black") +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
facet_wrap(~study_motivation) +
theme(
legend.position = c(0.8, 0.9), # Positioning the legend in the top-left corner within the plot
legend.justification = c(0, 1) # Ensuring the legend box aligns properly at the top-left corner
) +
labs(
x = "",
y = "Number of studies"
) +
theme()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.doses_fig
setwd(figures_path)
ggsave("comp_doses_fig.pdf", plot = doses_fig, width = 8.3, height = 11.7/3)## 9.8 Concentrations
Here I will have a look at the min and max and range of doses used in the database. For the MS, I am including only studies that reported in a mass to water volume measure so we can compare standardised unites (ug/L). This was the most common reporting methods (62% of all data; 1090 total).
nrow <- EIPAAB_database %>%
nrow()
EIPAAB_database %>%
dplyr::group_by(compound_min_dose_unit_std) %>%
dplyr::reframe(n = n(),
prop = n/nrow) %>%
dplyr::arrange(desc(n))## # A tibble: 7 × 3
## compound_min_dose_unit_std n prop
## <chr> <int> <dbl>
## 1 ug/L 1076 0.618
## 2 uM 397 0.228
## 3 <NA> 229 0.132
## 4 uM/L 25 0.0144
## 5 ppm 9 0.00517
## 6 uL/L 2 0.00115
## 7 ug/g 2 0.00115Summary of the minimum concentration used (where reported in mass to volume)
EIPAAB_database <- EIPAAB_database %>%
dplyr::mutate(range = compound_max_dose_std - compound_min_dose_std)EIPAAB_database %>%
dplyr::filter(compound_min_dose_unit_std == "ug/L", compound_max_dose_unit_std == "ug/L") %>%
dplyr::mutate(range = compound_max_dose_std - compound_min_dose_std) %>%
dplyr::group_by(study_motivation) %>%
dplyr::reframe(median_min = median(compound_min_dose_std, na.rm = T),
sd_min = sd(compound_min_dose_std, na.rm = T),
min_min = min(compound_min_dose_std, na.rm = T),
max_min = max(compound_min_dose_std, na.rm = T),
median_max = median(compound_max_dose_std, na.rm = T),
sd_max = sd(compound_max_dose_std, na.rm = T),
min_max = min(compound_max_dose_std, na.rm = T),
max_max = max(compound_max_dose_std, na.rm = T),
median_range = median(range, na.rm = T),
sd_range = sd(range, na.rm = T),
min_range = min(range, na.rm = T),
max_range = max(range, na.rm = T))## # A tibble: 3 × 13
## study_motivation median_min sd_min min_min max_min median_max sd_max min_max
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Environmental 0.995 25256. 3.13e-6 5 e5 100 1.41e5 0.001
## 2 Medical 1000 74501. 5 e-2 5.40e5 5000 2.07e5 0.05
## 3 Basic research 3000 5301683. 1 e-2 6 e7 10000 5.30e6 0.01
## # ℹ 5 more variables: max_max <dbl>, median_range <dbl>, sd_range <dbl>,
## # min_range <dbl>, max_range <dbl>A plot for minimum doses, its on the log axis because the distribution is highly skewed motivation_colour_theme
motivation_colour_theme <- c("#60BD6C", "#D359A1", "#3C82C4")
min_conc_fig <- EIPAAB_database %>%
dplyr::filter(compound_min_dose_unit_std == "ug/L") %>%
ggplot(aes(x=log(compound_min_dose_std), fill = study_motivation, colour = study_motivation)) +
stat_slab(alpha = 0.6, linewidth = 1.5, colour = NA) +
stat_pointinterval(point_interval = "median_qi",
position = position_dodge(width = .4, preserve = "single"),
.width = c(0.89, 0.95)) +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation", guide = 'none') +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
labs(
x = "Log10 minimum dose (ug/L)",
y = "Density"
) +
theme(legend.position="bottom")
min_conc_fig## Warning: Removed 8 rows containing missing values or values outside the scale range
## (`stat_slabinterval()`).
## Removed 8 rows containing missing values or values outside the scale range
## (`stat_slabinterval()`).
setwd(figures_path)
ggsave("comp_min_conc_fig.pdf", plot = min_conc_fig, width = 5, height = 6)A summary table so we can see what the corresponding raw values are in the plot
min_conc_summary <- EIPAAB_database %>%
dplyr::filter(compound_min_dose_unit_std == "ug/L") %>%
dplyr::group_by(study_motivation) %>%
dplyr::summarise(
median = median(log(compound_min_dose_std), na.rm = TRUE),
lower_89 = quantile(log(compound_min_dose_std), probs = 0.11, na.rm = TRUE),
upper_89 = quantile(log(compound_min_dose_std), probs = 0.89, na.rm = TRUE),
lower_95 = quantile(log(compound_min_dose_std), probs = 0.05, na.rm = TRUE),
upper_95 = quantile(log(compound_min_dose_std), probs = 0.95, na.rm = TRUE),
.groups = "drop"
) %>%
# Transform to a format suitable for ggplot annotation
pivot_longer(cols = -study_motivation, names_to = "stat", values_to = "value") %>%
dplyr::mutate(vaule_raw = format(exp(value), scientific = FALSE))
min_conc_summary## # A tibble: 15 × 4
## study_motivation stat value vaule_raw
## <fct> <chr> <dbl> <chr>
## 1 Environmental median -0.00503 " 0.994987437"
## 2 Environmental lower_89 -5.41 " 0.004456651"
## 3 Environmental upper_89 5.54 " 254.538265740"
## 4 Environmental lower_95 -6.91 " 0.001000000"
## 5 Environmental upper_95 8.35 " 4240.690713691"
## 6 Medical median 6.91 " 1000.000000000"
## 7 Medical lower_89 3.10 " 22.211490447"
## 8 Medical upper_89 10.3 " 30000.000000000"
## 9 Medical lower_95 0 " 1.000000000"
## 10 Medical upper_95 11.5 "100000.000000000"
## 11 Basic research median 8.01 " 3000.000000000"
## 12 Basic research lower_89 -0.920 " 0.398400829"
## 13 Basic research upper_89 10.8 " 50802.311370061"
## 14 Basic research lower_95 -2.30 " 0.100000000"
## 15 Basic research upper_95 11.5 "100000.000000000"A plot for maximum doses, its on the log axis because the distribution is highly skewed motivation_colour_theme
motivation_colour_theme <- c("#60BD6C", "#D359A1", "#3C82C4")
max_conc_fig <- EIPAAB_database %>%
dplyr::filter(compound_min_dose_unit_std == "ug/L") %>%
ggplot(aes(x=log(compound_max_dose_std), fill = study_motivation, colour = study_motivation)) +
stat_slab(alpha = 0.6, linewidth = 1.5, colour = NA) +
stat_pointinterval(point_interval = "median_qi",
position = position_dodge(width = .4, preserve = "single"),
.width = c(0.89, 0.95)) +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation", guide = 'none') +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
labs(
x = "Log10 maximum dose (ug/L)",
y = "Density"
) +
theme(legend.position="bottom")
max_conc_fig## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`stat_slabinterval()`).
## Removed 7 rows containing missing values or values outside the scale range
## (`stat_slabinterval()`).
setwd(figures_path)
ggsave("comp_max_conc_fig.pdf", plot = max_conc_fig, width = 5, height = 6)A summary table so we can see what the corresponding raw values are in the plot
max_conc_summary <- EIPAAB_database %>%
dplyr::filter(compound_min_dose_unit_std == "ug/L") %>%
dplyr::group_by(study_motivation) %>%
dplyr::summarise(
median = median(log(compound_max_dose_std), na.rm = TRUE),
lower_89 = quantile(log(compound_max_dose_std), probs = 0.11, na.rm = TRUE),
upper_89 = quantile(log(compound_max_dose_std), probs = 0.89, na.rm = TRUE),
lower_95 = quantile(log(compound_max_dose_std), probs = 0.05, na.rm = TRUE),
upper_95 = quantile(log(compound_max_dose_std), probs = 0.95, na.rm = TRUE),
.groups = "drop"
) %>%
# Transform to a format suitable for ggplot annotation
pivot_longer(cols = -study_motivation, names_to = "stat", values_to = "value") %>%
dplyr::mutate(vaule_raw = format(exp(value), scientific = FALSE))
max_conc_summary## # A tibble: 15 × 4
## study_motivation stat value vaule_raw
## <fct> <chr> <dbl> <chr>
## 1 Environmental median 4.61 " 100.000000000"
## 2 Environmental lower_89 -3.00 " 0.049652545"
## 3 Environmental upper_89 10.1 " 23247.868374564"
## 4 Environmental lower_95 -4.62 " 0.009873911"
## 5 Environmental upper_95 11.5 "100000.000000000"
## 6 Medical median 8.52 " 5000.000000000"
## 7 Medical lower_89 4.42 " 82.775528294"
## 8 Medical upper_89 11.5 "100000.000000000"
## 9 Medical lower_95 2.98 " 19.686403036"
## 10 Medical upper_95 12.9 "408740.139635277"
## 11 Basic research median 9.21 " 10000.000000000"
## 12 Basic research lower_89 0.895 " 2.446887175"
## 13 Basic research upper_89 11.5 "100000.000000000"
## 14 Basic research lower_95 -2.30 " 0.100000000"
## 15 Basic research upper_95 12.6 "300000.000000000"A plot for the range of doses, its on the log axis because the distribution is highly skewed. This includes only studies that had more then one dose and reported concentration in a mass to volume metric.
motivation_colour_theme <- c("#60BD6C", "#D359A1", "#3C82C4")
range_conc_fig <- EIPAAB_database %>%
dplyr::filter(compound_min_dose_unit_std == "ug/L") %>%
dplyr::filter(range > 0) %>%
ggplot(aes(x=log(range), fill = study_motivation, colour = study_motivation)) +
stat_slab(alpha = 0.6, linewidth = 1.5, colour = NA) +
stat_pointinterval(point_interval = "median_qi",
position = position_dodge(width = .4, preserve = "single"),
.width = c(0.89, 0.95)) +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation", guide = 'none') +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
labs(
x = "Log10 range (ug/L)",
y = "Density"
) +
theme(legend.position="bottom")
range_conc_fig
setwd(figures_path)
ggsave("comp_range_conc_fig.pdf", plot = range_conc_fig, width = 5, height = 6)A summary table so we can see what the corresponding raw values are in the plot
range_conc_summary <- EIPAAB_database %>%
dplyr::filter(compound_min_dose_unit_std == "ug/L",
range > 0) %>%
dplyr::group_by(study_motivation) %>%
summarise(
median = median(log(range), na.rm = TRUE),
lower_89 = quantile(log(range), probs = 0.055, na.rm = TRUE),
upper_89 = quantile(log(range), probs = 0.945, na.rm = TRUE),
lower_95 = quantile(log(range), probs = 0.025, na.rm = TRUE),
upper_95 = quantile(log(range), probs = 0.975, na.rm = TRUE)
) %>%
# Transform to a format suitable for ggplot annotation
pivot_longer(cols = -study_motivation, names_to = "stat", values_to = "value") %>%
dplyr::mutate(vaule_raw = format(exp(value), scientific = FALSE))
range_conc_summary## # A tibble: 15 × 4
## study_motivation stat value vaule_raw
## <fct> <chr> <dbl> <chr>
## 1 Environmental median 5.38 " 217.80000000"
## 2 Environmental lower_89 -3.00 " 0.05000498"
## 3 Environmental upper_89 11.5 " 99995.93990911"
## 4 Environmental lower_95 -4.07 " 0.01704190"
## 5 Environmental upper_95 13.1 "491792.33880353"
## 6 Medical median 9.10 " 9000.00000000"
## 7 Medical lower_89 4.33 " 75.72276785"
## 8 Medical upper_89 13.2 "539495.65697997"
## 9 Medical lower_95 3.42 " 30.45013206"
## 10 Medical upper_95 13.6 "846549.05789480"
## 11 Basic research median 9.85 " 19000.00000000"
## 12 Basic research lower_89 0.0872 " 1.09108007"
## 13 Basic research upper_89 12.5 "270000.00000000"
## 14 Basic research lower_95 -0.0102 " 0.98987944"
## 15 Basic research upper_95 12.6 "299042.91267704"env_min_conc_fig <- EIPAAB_database %>%
dplyr::filter(study_motivation == "Environmental") %>%
dplyr::filter(compound_min_dose_unit_std == "ug/L") %>%
ggplot(aes(x=log(compound_min_dose_std))) +
stat_slab(aes(alpha = 0.8, linewidth = 1.5)) +
stat_pointinterval(point_interval = "median_qi",
position = position_dodge(width = .4, preserve = "single"),
.width = c(0.89, 0.95)) +
theme_classic() +
theme(legend.position="none")
env_min_conc_fig## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`stat_slabinterval()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`stat_slabinterval()`).
A summary table so we can see what the corresponding raw values are in
the plot
env_conc_summary <- EIPAAB_database %>%
filter(study_motivation == "Environmental",
compound_min_dose_unit_std == "ug/L") %>%
summarise(
median = median(log(compound_min_dose_std), na.rm = TRUE),
lower_89 = quantile(log(compound_min_dose_std), probs = 0.055, na.rm = TRUE),
upper_89 = quantile(log(compound_min_dose_std), probs = 0.945, na.rm = TRUE),
lower_95 = quantile(log(compound_min_dose_std), probs = 0.025, na.rm = TRUE),
upper_95 = quantile(log(compound_min_dose_std), probs = 0.975, na.rm = TRUE)
) %>%
# Transform to a format suitable for ggplot annotation
pivot_longer(cols = everything(), names_to = "stat", values_to = "value") %>%
dplyr::mutate(vaule_raw = format(exp(value), scientific = FALSE))
env_conc_summary## # A tibble: 5 × 3
## stat value vaule_raw
## <chr> <dbl> <chr>
## 1 median -0.00503 " 0.9949874371"
## 2 lower_89 -6.91 " 0.0010000000"
## 3 upper_89 7.79 " 2425.8399279662"
## 4 lower_95 -7.49 " 0.0005588055"
## 5 upper_95 9.67 "15901.5600424659"14.9 Exposure location
Where the exposure itself was conducted
EIPAAB_database %>%
dplyr::group_by(compound_exposure_location) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(total = sum(n),
perecent = round(n/total*100,1))## # A tibble: 3 × 4
## compound_exposure_location n total perecent
## <chr> <int> <int> <dbl>
## 1 Indoor laboratory setting or assumed indoors 1730 1740 99.4
## 2 Outdoor natural setting 4 1740 0.2
## 3 Outdoor restricted setting (cannot interact with wild sp… 6 1740 0.315 Behaviour
15.1 Counts of catagoires
First I will make a new variable called beahv_catgory_n, which will look at how many of our 10 broad behavioural categories were measured in the article.
The 10 over-arching categories were: (1) movement and locomotion, (2) pre-mating and mating behaviour, (3) post-mating behaviour, (4) aggression, (5) sociality, (6) cognition and learning, (7) anxiety and boldness, (8) foraging and feeding, (9) antipredator behaviour, and (10) other behaviours not categorised
This will take the some of all the behaviour categories., so can range from 1 to 10 for a single behavioural category to all categories.
EIPAAB_database <- EIPAAB_database %>%
dplyr::mutate(behav_category_n = rowSums(across(starts_with("behav_") & ends_with("_boolean"))))The majority of evidence seems to be based on a single behavioural category
EIPAAB_database %>%
dplyr::group_by(behav_category_n) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(percent = round(n/sum(n)*100,1))## # A tibble: 6 × 3
## behav_category_n n percent
## <dbl> <int> <dbl>
## 1 1 1206 69.3
## 2 2 400 23
## 3 3 115 6.6
## 4 4 16 0.9
## 5 5 2 0.1
## 6 7 1 0.1Is this the same by study motivation
EIPAAB_database %>%
dplyr::group_by(behav_category_n, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1))## # A tibble: 14 × 5
## behav_category_n study_motivation n total_motivation percent
## <dbl> <fct> <int> <int> <dbl>
## 1 1 Environmental 593 858 69.1
## 2 1 Medical 379 519 73
## 3 1 Basic research 234 363 64.5
## 4 2 Environmental 180 858 21
## 5 2 Medical 110 519 21.2
## 6 2 Basic research 110 363 30.3
## 7 3 Environmental 75 858 8.7
## 8 3 Medical 25 519 4.8
## 9 3 Basic research 15 363 4.1
## 10 4 Environmental 8 858 0.9
## 11 4 Medical 4 519 0.8
## 12 4 Basic research 4 363 1.1
## 13 5 Environmental 2 858 0.2
## 14 7 Medical 1 519 0.2Here I make a dataframe where I have pivoted the data to long formate based on each of the 10 behaviour categories. This dataframe can be used to ask more spefic questiosn about the relationship between species, compound, and behaviour.
But first let’s use it to see what behaviours are most common overall all, and within each study motivation.
binary_behav <- EIPAAB_database %>%
dplyr::select((starts_with("behav_") & ends_with("_boolean"))) %>%
colnames()
PICO_long <- EIPAAB_database %>%
tidyr::pivot_longer(.,
cols = all_of(binary_behav),
names_to = "behav_category",
values_to = "value") %>%
dplyr::select(article_id, study_motivation, species_name, species_class,
compound_name, compound_atc_level_3, behav_category, value) %>%
dplyr::mutate(behav_category = behav_category %>% str_remove("behav_") %>% str_remove("_boolean"))
PICO_long## # A tibble: 17,400 × 8
## article_id study_motivation species_name species_class compound_name
## <chr> <fct> <chr> <chr> <chr>
## 1 236660465 Environmental Danio rerio Actinopterygii Buspirone
## 2 236660465 Environmental Danio rerio Actinopterygii Buspirone
## 3 236660465 Environmental Danio rerio Actinopterygii Buspirone
## 4 236660465 Environmental Danio rerio Actinopterygii Buspirone
## 5 236660465 Environmental Danio rerio Actinopterygii Buspirone
## 6 236660465 Environmental Danio rerio Actinopterygii Buspirone
## 7 236660465 Environmental Danio rerio Actinopterygii Buspirone
## 8 236660465 Environmental Danio rerio Actinopterygii Buspirone
## 9 236660465 Environmental Danio rerio Actinopterygii Buspirone
## 10 236660465 Environmental Danio rerio Actinopterygii Buspirone
## # ℹ 17,390 more rows
## # ℹ 3 more variables: compound_atc_level_3 <chr>, behav_category <chr>,
## # value <int>behav_overall <- PICO_long %>%
dplyr::group_by(behav_category) %>%
reframe(n = sum(value)) %>%
dplyr::mutate(percent = round(n/sum(n)*100, 1)) %>%
dplyr::arrange(desc(n))
behav_overall## # A tibble: 10 × 3
## behav_category n percent
## <chr> <int> <dbl>
## 1 movement 983 40.4
## 2 boldness 568 23.4
## 3 foraging 190 7.8
## 4 agression 145 6
## 5 sociality 143 5.9
## 6 mating 122 5
## 7 noncat 96 3.9
## 8 cognition 90 3.7
## 9 antipredator 85 3.5
## 10 post_mating 10 0.4behav_motivation <- PICO_long %>%
dplyr::group_by(behav_category, study_motivation) %>%
dplyr::summarise(n = sum(value), .groups = 'drop') %>%
tidyr::complete(behav_category, study_motivation, fill = list(n = 0)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation) %>%
dplyr::arrange(desc(study_motivation))
behav_overall <- behav_motivation %>%
dplyr::group_by(behav_category) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(percent = round(n/sum(n)*100,1),
study_motivation = "Overall")
behav_motivation <- rbind(behav_overall, behav_motivation)
behav_motivation## # A tibble: 40 × 4
## behav_category n percent study_motivation
## <chr> <int> <dbl> <chr>
## 1 agression 145 6 Overall
## 2 antipredator 85 3.5 Overall
## 3 boldness 568 23.4 Overall
## 4 cognition 90 3.7 Overall
## 5 foraging 190 7.8 Overall
## 6 mating 122 5 Overall
## 7 movement 983 40.4 Overall
## 8 noncat 96 3.9 Overall
## 9 post_mating 10 0.4 Overall
## 10 sociality 143 5.9 Overall
## # ℹ 30 more rowsmotivation_colour_theme <- c("grey", "#60BD6C", "#D359A1", "#3C82C4")
behav_order <- c("movement", "boldness", "foraging", "antipredator", "mating", "post_mating", "agression", "sociality", "cognition", "noncat")
study_motivation_order <- c("Overall", "Environmental", "Medical", "Basic research")
behav_motivation_fig <- behav_motivation %>%
dplyr::mutate(behav_category = factor(behav_category, levels = rev(behav_order)),
study_motivation = factor(study_motivation, levels = study_motivation_order)) %>%
ggplot(aes(x=behav_category, y=percent, colour = study_motivation,
fill = study_motivation), group = study_motivation) +
geom_col(width = 0.1, colour = NA) +
geom_point(size = 3) +
geom_text(aes(label = percent), vjust = -0.3, size=3.5, color="black") +
theme_classic() +
facet_grid(cols = vars(study_motivation)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
coord_flip() +
labs(
x = "",
y = "Percentage"
) +
theme(
plot.title = element_text(size = 11)
)
behav_motivation_fig
setwd(figures_path)
ggsave("behav_motivation_fig.pdf", plot = behav_motivation_fig, width = 10, height = 5)15.2 Behaviour sub-categories
behav_select <- EIPAAB_database %>%
dplyr::select((starts_with("behav_") & !ends_with("_boolean") & !ends_with("is_social_context") & !ends_with("test_location") & !ends_with("category_n"))) %>%
colnames()
behav_sub_cat_long <- EIPAAB_database %>%
tidyr::pivot_longer(.,
cols = all_of(behav_select),
names_to = "parent_category",
values_to = "sub_category") %>%
dplyr::select(article_id, study_motivation, species_name, species_class,
compound_name, compound_atc_level_3, parent_category, sub_category) %>%
dplyr::mutate(parent_category = parent_category %>% str_remove("behav_")) %>%
tidyr::separate_rows(sub_category, sep = ";") %>%
dplyr::filter(!is.na(sub_category))behav_sub_cat_summary <- behav_sub_cat_long %>%
dplyr::group_by(study_motivation, parent_category, sub_category) %>%
dplyr::summarise(n_sub_cat = n(), .groups = 'drop') %>%
dplyr::group_by(study_motivation, parent_category) %>%
dplyr::mutate(n_parent = sum(n_sub_cat)) %>%
dplyr::ungroup() %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(n_motivation = sum(n_sub_cat)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent_sub_cat = n_sub_cat/n_parent,
percent_parent = n_parent/n_motivation)
behav_sub_cat_summary## # A tibble: 155 × 8
## study_motivation parent_category sub_category n_sub_cat n_parent n_motivation
## <fct> <chr> <chr> <int> <int> <int>
## 1 Environmental agression aggression … 27 70 1511
## 2 Environmental agression aggression … 10 70 1511
## 3 Environmental agression aggression … 20 70 1511
## 4 Environmental agression aggression … 7 70 1511
## 5 Environmental agression locomotor a… 6 70 1511
## 6 Environmental antipredator locomotor a… 22 100 1511
## 7 Environmental antipredator response to… 10 100 1511
## 8 Environmental antipredator response to… 6 100 1511
## 9 Environmental antipredator response to… 20 100 1511
## 10 Environmental antipredator response to… 42 100 1511
## # ℹ 145 more rows
## # ℹ 2 more variables: percent_sub_cat <dbl>, percent_parent <dbl>ring_plot_subcat <- behav_sub_cat_summary %>%
dplyr::group_by(study_motivation, parent_category) %>%
dplyr::mutate(ymax = cumsum(percent_sub_cat),
ymin = lag(ymax,1),
ymin = if_else(is.na(ymin), 0, ymin),
labelPosition = (ymax+ymin)/2,
label = paste0(sub_category, "\n (n = ", n_sub_cat, ")")) %>%
dplyr::ungroup()15.2.1 Movement plots
First making a complete dataset (adding zeros for missing sub-categories. in each motivation), and ordering by overall prevalence of sub-categories.
sub_category_order <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "movement") %>%
dplyr::group_by(sub_category) %>%
dplyr::reframe(n = sum(n_sub_cat)) %>%
dplyr::arrange(n) %>%
dplyr::pull(sub_category)
movement_subcat <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "movement") %>%
dplyr::select(study_motivation, sub_category, percent_sub_cat, n_sub_cat, percent_parent) %>%
tidyr::complete(sub_category, study_motivation, fill = list(percent_sub_cat = 0, n_sub_cat = 0)) %>%
dplyr::mutate(sub_category = factor(sub_category, levels = sub_category_order))beh_movement_subcat_fig <- movement_subcat %>%
dplyr::mutate(percent_sub_cat = round(percent_sub_cat,3)*100) %>%
ggplot(aes(x=sub_category, y=percent_sub_cat, colour = study_motivation, fill = study_motivation, group = study_motivation)) +
geom_col(position = position_dodge(width = 0.8), width = 0.1) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_text(aes(label = percent_sub_cat), hjust=-0.6, size=3.5, color="black", position = position_dodge(width = 0.8)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
coord_flip() +
labs(
x = "",
y = "Percentage of data"
) +
theme(
legend.position = "none"
)
beh_movement_subcat_fig
n_subcat <- movement_subcat %>%
dplyr::distinct(sub_category) %>%
nrow(.)/10
setwd(figures_path)
ggsave("beh_movement_subcat_fig.pdf", plot = beh_movement_subcat_fig, width = 10, height = 11.7*n_subcat)If you would like to make a doughnut chart here’s the code. However, for categories that have 5 or more sub-categories like movement I don’t think this is the clearest way to present the data.
movement_subcat %>%
dplyr::arrange(sub_category) %>%
dplyr::arrange(study_motivation) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(ymax = cumsum(percent_sub_cat),
ymin = lag(ymax,1),
ymin = if_else(is.na(ymin), 0, ymin),
labelPosition = (ymax+ymin)/2,
label = if_else(n_sub_cat == 0, NA, n_sub_cat)) %>%
dplyr::ungroup() %>%
ggplot(aes(ymax=ymax, ymin=ymin, xmax=4, xmin=3, fill=sub_category)) +
geom_rect() +
coord_polar(theta="y") +
geom_label(x=4, aes(y=labelPosition, label=label), size=3, alpha = 0.8) +
facet_wrap(~study_motivation) +
xlim(c(2, 5)) +
theme_void() +
theme(legend.position = "bottom")## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_label()`).
15.2.2 Boldness plots
sub_category_order <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "boldness") %>%
dplyr::group_by(sub_category) %>%
dplyr::reframe(n = sum(n_sub_cat)) %>%
dplyr::arrange(n) %>%
dplyr::pull(sub_category)
boldness_subcat <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "boldness") %>%
dplyr::select(study_motivation, sub_category, percent_sub_cat, n_sub_cat, percent_parent) %>%
tidyr::complete(sub_category, study_motivation, fill = list(percent_sub_cat = 0, n_sub_cat = 0)) %>%
dplyr::mutate(sub_category = factor(sub_category, levels = sub_category_order))beh_boldness_subcat_fig <- boldness_subcat %>%
dplyr::mutate(percent_sub_cat = round(percent_sub_cat,3)*100) %>%
ggplot(aes(x=sub_category, y=percent_sub_cat, colour = study_motivation, fill = study_motivation, group = study_motivation)) +
geom_col(position = position_dodge(width = 0.8), width = 0.1) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_text(aes(label = percent_sub_cat), hjust=-0.6, size=3.5, color="black", position = position_dodge(width = 0.8)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
coord_flip() +
labs(
x = "",
y = "Percentage of data"
) +
theme(
legend.position = "none"
)
beh_boldness_subcat_fig
n_subcat <- boldness_subcat %>%
dplyr::distinct(sub_category) %>%
nrow(.)/10
setwd(figures_path)
ggsave("beh_boldness_subcat_fig.pdf", plot = beh_boldness_subcat_fig, width = 10, height = 11.7*n_subcat)15.2.3 Foraging plots
sub_category_order <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "foraging") %>%
dplyr::group_by(sub_category) %>%
dplyr::reframe(n = sum(n_sub_cat)) %>%
dplyr::arrange(n) %>%
dplyr::pull(sub_category)
foraging_subcat <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "foraging") %>%
dplyr::select(study_motivation, sub_category, percent_sub_cat, n_sub_cat, percent_parent) %>%
tidyr::complete(sub_category, study_motivation, fill = list(percent_sub_cat = 0, n_sub_cat = 0)) %>%
dplyr::mutate(sub_category = factor(sub_category, levels = sub_category_order))beh_foraging_subcat_fig <- foraging_subcat %>%
dplyr::mutate(percent_sub_cat = round(percent_sub_cat,3)*100) %>%
ggplot(aes(x=sub_category, y=percent_sub_cat, colour = study_motivation, fill = study_motivation, group = study_motivation)) +
geom_col(position = position_dodge(width = 0.8), width = 0.1) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_text(aes(label = percent_sub_cat), hjust=-0.6, size=3.5, color="black", position = position_dodge(width = 0.8)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
coord_flip() +
labs(
x = "",
y = "Percentage of data"
) +
theme(
legend.position = "none"
)
beh_foraging_subcat_fig
n_subcat <- foraging_subcat %>%
dplyr::distinct(sub_category) %>%
nrow(.)/10
setwd(figures_path)
ggsave("beh_foraging_subcat_fig.pdf", plot = beh_foraging_subcat_fig, width = 10, height = 11.7*n_subcat)15.2.4 Antipredator plots
sub_category_order <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "antipredator") %>%
dplyr::group_by(sub_category) %>%
dplyr::reframe(n = sum(n_sub_cat)) %>%
dplyr::arrange(n) %>%
dplyr::pull(sub_category)
antipredator_subcat <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "antipredator") %>%
dplyr::select(study_motivation, sub_category, percent_sub_cat, n_sub_cat, percent_parent) %>%
tidyr::complete(sub_category, study_motivation, fill = list(percent_sub_cat = 0, n_sub_cat = 0)) %>%
dplyr::mutate(sub_category = factor(sub_category, levels = sub_category_order))beh_antipredator_subcat_fig <- antipredator_subcat %>%
dplyr::mutate(percent_sub_cat = round(percent_sub_cat,3)*100) %>%
ggplot(aes(x=sub_category, y=percent_sub_cat, colour = study_motivation, fill = study_motivation, group = study_motivation)) +
geom_col(position = position_dodge(width = 0.8), width = 0.1) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_text(aes(label = percent_sub_cat), hjust=-0.6, size=3.5, color="black", position = position_dodge(width = 0.8)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
coord_flip() +
labs(
x = "",
y = "Percentage of data"
) +
theme(
legend.position = "none"
)
beh_antipredator_subcat_fig
n_subcat <- antipredator_subcat %>%
dplyr::distinct(sub_category) %>%
nrow(.)/10
setwd(figures_path)
ggsave("beh_antipredator_subcat_fig.pdf", plot = beh_antipredator_subcat_fig, width = 10, height = 11.7*n_subcat)15.2.5 Mating plots
sub_category_order <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "mating") %>%
dplyr::group_by(sub_category) %>%
dplyr::reframe(n = sum(n_sub_cat)) %>%
dplyr::arrange(n) %>%
dplyr::pull(sub_category)
mating_subcat <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "mating") %>%
dplyr::select(study_motivation, sub_category, percent_sub_cat, n_sub_cat, percent_parent) %>%
tidyr::complete(sub_category, study_motivation, fill = list(percent_sub_cat = 0, n_sub_cat = 0)) %>%
dplyr::mutate(sub_category = factor(sub_category, levels = sub_category_order))beh_mating_subcat_fig <- mating_subcat %>%
dplyr::mutate(percent_sub_cat = round(percent_sub_cat,3)*100) %>%
ggplot(aes(x=sub_category, y=percent_sub_cat, colour = study_motivation, fill = study_motivation, group = study_motivation)) +
geom_col(position = position_dodge(width = 0.8), width = 0.1) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_text(aes(label = percent_sub_cat), hjust=-0.6, size=3.5, color="black", position = position_dodge(width = 0.8)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
coord_flip() +
labs(
x = "",
y = "Percentage of data"
) +
theme(
legend.position = "none"
)
beh_mating_subcat_fig
n_subcat <- mating_subcat %>%
dplyr::distinct(sub_category) %>%
nrow(.)/10
setwd(figures_path)
ggsave("beh_mating_subcat_fig.pdf", plot = beh_mating_subcat_fig, width = 10, height = 11.7*n_subcat)15.2.6 Post mating plots
sub_category_order <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "post_mating") %>%
dplyr::group_by(sub_category) %>%
dplyr::reframe(n = sum(n_sub_cat)) %>%
dplyr::arrange(n) %>%
dplyr::pull(sub_category)
post_mating_subcat <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "post_mating") %>%
dplyr::select(study_motivation, sub_category, percent_sub_cat, n_sub_cat, percent_parent) %>%
tidyr::complete(sub_category, study_motivation, fill = list(percent_sub_cat = 0, n_sub_cat = 0)) %>%
dplyr::mutate(sub_category = factor(sub_category, levels = sub_category_order))beh_post_mating_subcat_fig <- post_mating_subcat %>%
dplyr::mutate(percent_sub_cat = round(percent_sub_cat,3)*100) %>%
ggplot(aes(x=sub_category, y=percent_sub_cat, colour = study_motivation, fill = study_motivation, group = study_motivation)) +
geom_col(position = position_dodge(width = 0.8), width = 0.1) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_text(aes(label = percent_sub_cat), hjust=-0.6, size=3.5, color="black", position = position_dodge(width = 0.8)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
coord_flip() +
labs(
x = "",
y = "Percentage of data"
) +
theme(
legend.position = "none"
)
beh_post_mating_subcat_fig
n_subcat <- post_mating_subcat %>%
dplyr::distinct(sub_category) %>%
nrow(.)/10
setwd(figures_path)
ggsave("beh_post_mating_subcat_fig.pdf", plot = beh_post_mating_subcat_fig, width = 10, height = 11.7*n_subcat)15.2.7 Agression plots
sub_category_order <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "agression") %>%
dplyr::group_by(sub_category) %>%
dplyr::reframe(n = sum(n_sub_cat)) %>%
dplyr::arrange(n) %>%
dplyr::pull(sub_category)
agression_subcat <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "agression") %>%
dplyr::select(study_motivation, sub_category, percent_sub_cat, n_sub_cat, percent_parent) %>%
tidyr::complete(sub_category, study_motivation, fill = list(percent_sub_cat = 0, n_sub_cat = 0)) %>%
dplyr::mutate(sub_category = factor(sub_category, levels = sub_category_order))beh_agression_subcat_fig <- agression_subcat %>%
dplyr::mutate(percent_sub_cat = round(percent_sub_cat,3)*100) %>%
ggplot(aes(x=sub_category, y=percent_sub_cat, colour = study_motivation, fill = study_motivation, group = study_motivation)) +
geom_col(position = position_dodge(width = 0.8), width = 0.1) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_text(aes(label = percent_sub_cat), hjust=-0.6, size=3.5, color="black", position = position_dodge(width = 0.8)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
coord_flip() +
labs(
x = "",
y = "Percentage of data"
) +
theme(
legend.position = "none"
)
beh_agression_subcat_fig
n_subcat <- agression_subcat %>%
dplyr::distinct(sub_category) %>%
nrow(.)/10
setwd(figures_path)
ggsave("beh_agression_subcat_fig.pdf", plot = beh_agression_subcat_fig, width = 10, height = 11.7*n_subcat)15.2.9 Cognition plots
sub_category_order <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "cognition") %>%
dplyr::group_by(sub_category) %>%
dplyr::reframe(n = sum(n_sub_cat)) %>%
dplyr::arrange(n) %>%
dplyr::pull(sub_category)
cognition_subcat <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "cognition") %>%
dplyr::select(study_motivation, sub_category, percent_sub_cat, n_sub_cat, percent_parent) %>%
tidyr::complete(sub_category, study_motivation, fill = list(percent_sub_cat = 0, n_sub_cat = 0)) %>%
dplyr::mutate(sub_category = factor(sub_category, levels = sub_category_order))beh_cognition_subcat_fig <- cognition_subcat %>%
dplyr::mutate(percent_sub_cat = round(percent_sub_cat,3)*100) %>%
ggplot(aes(x=sub_category, y=percent_sub_cat, colour = study_motivation, fill = study_motivation, group = study_motivation)) +
geom_col(position = position_dodge(width = 0.8), width = 0.1) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_text(aes(label = percent_sub_cat), hjust=-0.6, size=3.5, color="black", position = position_dodge(width = 0.8)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
coord_flip() +
labs(
x = "",
y = "Percentage of data"
) +
theme(
legend.position = "none"
)
beh_cognition_subcat_fig
n_subcat <- cognition_subcat %>%
dplyr::distinct(sub_category) %>%
nrow(.)/10
setwd(figures_path)
ggsave("beh_cognition_subcat_fig.pdf", plot = beh_cognition_subcat_fig, width = 10, height = 11.7*n_subcat)15.2.10 Non-categories plots
sub_category_order <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "noncat") %>%
dplyr::group_by(sub_category) %>%
dplyr::reframe(n = sum(n_sub_cat)) %>%
dplyr::arrange(n) %>%
dplyr::pull(sub_category)
noncat_subcat <- behav_sub_cat_summary %>%
dplyr::filter(parent_category == "noncat") %>%
dplyr::select(study_motivation, sub_category, percent_sub_cat, n_sub_cat, percent_parent) %>%
tidyr::complete(sub_category, study_motivation, fill = list(percent_sub_cat = 0, n_sub_cat = 0)) %>%
dplyr::mutate(sub_category = factor(sub_category, levels = sub_category_order))beh_noncat_subcat_fig <- noncat_subcat %>%
dplyr::mutate(percent_sub_cat = round(percent_sub_cat,3)*100) %>%
ggplot(aes(x=sub_category, y=percent_sub_cat, colour = study_motivation, fill = study_motivation, group = study_motivation)) +
geom_col(position = position_dodge(width = 0.8), width = 0.1) +
geom_point(position = position_dodge(width = 0.8), size = 3) +
geom_text(aes(label = percent_sub_cat), hjust=-0.6, size=3.5, color="black", position = position_dodge(width = 0.8)) +
scale_colour_manual(values = motivation_colour_theme, name = "Study motivation") +
scale_fill_manual(values = motivation_colour_theme, name = "Study motivation") +
theme_classic() +
coord_flip() +
labs(
x = "",
y = "Percentage of data"
) +
theme(
legend.position = "none"
)
beh_noncat_subcat_fig
n_subcat <- noncat_subcat %>%
dplyr::distinct(sub_category) %>%
nrow(.)/10
setwd(figures_path)
ggsave("beh_noncat_subcat_fig.pdf", plot = beh_noncat_subcat_fig, width = 10, height = 11.7*n_subcat)15.3 Behaviour location
Check where behaviour was meassured.
behav_location_summary <- EIPAAB_database %>%
dplyr::group_by(study_motivation, behav_test_location) %>%
dplyr::reframe(n = n()) %>%
tidyr::separate_rows(behav_test_location, sep = ";") %>%
dplyr::group_by(study_motivation, behav_test_location) %>%
dplyr::reframe(n = sum(n)) %>%
tidyr::complete(behav_test_location, study_motivation, fill = list(n = 0)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
behav_location_overall <- behav_location_summary %>%
dplyr::group_by(behav_test_location) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(percent = round(n/sum(n)*100,1),
study_motivation = "Overall")
behav_location_summary <- rbind(behav_location_overall, behav_location_summary) %>%
dplyr::arrange(study_motivation)
behav_location_summary## # A tibble: 12 × 4
## behav_test_location n percent study_motivation
## <chr> <int> <dbl> <chr>
## 1 indoor laboratory setting or assumed indoors 363 99.7 Basic research
## 2 outdoor natural setting 1 0.3 Basic research
## 3 outdoor restricted setting (cannot interact w… 0 0 Basic research
## 4 indoor laboratory setting or assumed indoors 852 98.7 Environmental
## 5 outdoor natural setting 8 0.9 Environmental
## 6 outdoor restricted setting (cannot interact w… 3 0.3 Environmental
## 7 indoor laboratory setting or assumed indoors 519 99.6 Medical
## 8 outdoor natural setting 1 0.2 Medical
## 9 outdoor restricted setting (cannot interact w… 1 0.2 Medical
## 10 indoor laboratory setting or assumed indoors 1734 99.2 Overall
## 11 outdoor natural setting 10 0.6 Overall
## 12 outdoor restricted setting (cannot interact w… 4 0.2 Overall15.5 Check how behaviour was meassured
Check how often behaviour was meassured in a social context
behav_behav_scoring_summary <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(study_motivation, validity_behav_scoring_method) %>%
dplyr::reframe(n = n()) %>%
tidyr::separate_rows(validity_behav_scoring_method, sep = ";") %>%
dplyr::group_by(study_motivation, validity_behav_scoring_method) %>%
dplyr::reframe(n = sum(n)) %>%
tidyr::complete(validity_behav_scoring_method, study_motivation, fill = list(n = 0)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
behav_behav_scoring_overall <- behav_behav_scoring_summary %>%
dplyr::group_by(validity_behav_scoring_method) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(percent = round(n/sum(n)*100,1),
study_motivation = "Overall")
behav_behav_scoring_summary <- rbind(behav_behav_scoring_overall, behav_behav_scoring_summary) %>%
dplyr::arrange(study_motivation)
behav_behav_scoring_summary## # A tibble: 32 × 4
## validity_behav_scoring_method n percent study_motivation
## <chr> <int> <dbl> <chr>
## 1 acoustic analysis software 0 0 Basic research
## 2 live scoring in real time 17 10 Basic research
## 3 manual or human scoring from videos or image 43 25.3 Basic research
## 4 not specified 35 20.6 Basic research
## 5 other 0 0 Basic research
## 6 quantifying food consumption 2 1.2 Basic research
## 7 sensory for physical movement 2 1.2 Basic research
## 8 supervised automated tracking approaches 71 41.8 Basic research
## 9 acoustic analysis software 1 0.2 Environmental
## 10 live scoring in real time 52 9.6 Environmental
## # ℹ 22 more rowsbehav_behav_scoring_summary %>%
dplyr::filter(study_motivation == "Overall")## # A tibble: 8 × 4
## validity_behav_scoring_method n percent study_motivation
## <chr> <int> <dbl> <chr>
## 1 acoustic analysis software 1 0.1 Overall
## 2 live scoring in real time 84 8.6 Overall
## 3 manual or human scoring from videos or image 259 26.6 Overall
## 4 not specified 221 22.7 Overall
## 5 other 1 0.1 Overall
## 6 quantifying food consumption 21 2.2 Overall
## 7 sensory for physical movement 7 0.7 Overall
## 8 supervised automated tracking approaches 378 38.9 Overall16 Linking PICO
Making a dataframe for a flow diagram (sankey plot)
PICO_df <- EIPAAB_database %>%
dplyr::mutate(behav_cat = case_when(
behav_movement_boolean == 1 ~ "Movement",
behav_boldness_boolean == 1 ~ "Boldness",
behav_foraging_boolean == 1 ~ "Foraging",
behav_antipredator_boolean == 1 ~ "Antipredator",
behav_mating_boolean == 1 ~ "Mating",
behav_post_mating_boolean == 1 ~ "Post mating",
behav_agression_boolean == 1 ~ "Agression",
behav_sociality_boolean == 1 ~ "Sociality",
behav_cognition_boolean == 1 ~ "Cognition",
behav_noncat_boolean == 1 ~ "Not categorised",
)) %>%
dplyr::select(study_motivation, compound_name, compound_atc_level_3, species_name, species_class, behav_cat)
PICO_df## study_motivation
## 1 Environmental
## 2 Environmental
## 3 Medical
## 4 Medical
## 5 Medical
## 6 Medical
## 7 Medical
## 8 Basic research
## 9 Medical
## 10 Environmental
## 11 Environmental
## 12 Environmental
## 13 Environmental
## 14 Medical
## 15 Environmental
## 16 Medical
## 17 Environmental
## 18 Basic research
## 19 Medical
## 20 Environmental
## 21 Environmental
## 22 Basic research
## 23 Medical
## 24 Medical
## 25 Medical
## 26 Environmental
## 27 Environmental
## 28 Environmental
## 29 Medical
## 30 Medical
## 31 Medical
## 32 Medical
## 33 Medical
## 34 Medical
## 35 Medical
## 36 Medical
## 37 Medical
## 38 Medical
## 39 Medical
## 40 Medical
## 41 Medical
## 42 Medical
## 43 Environmental
## 44 Environmental
## 45 Medical
## 46 Medical
## 47 Medical
## 48 Medical
## 49 Medical
## 50 Medical
## 51 Medical
## 52 Medical
## 53 Medical
## 54 Environmental
## 55 Environmental
## 56 Environmental
## 57 Environmental
## 58 Environmental
## 59 Basic research
## 60 Environmental
## 61 Environmental
## 62 Environmental
## 63 Environmental
## 64 Environmental
## 65 Environmental
## 66 Medical
## 67 Medical
## 68 Environmental
## 69 Environmental
## 70 Environmental
## 71 Medical
## 72 Medical
## 73 Environmental
## 74 Environmental
## 75 Environmental
## 76 Environmental
## 77 Environmental
## 78 Environmental
## 79 Environmental
## 80 Environmental
## 81 Environmental
## 82 Medical
## 83 Environmental
## 84 Environmental
## 85 Medical
## 86 Environmental
## 87 Environmental
## 88 Environmental
## 89 Environmental
## 90 Medical
## 91 Environmental
## 92 Medical
## 93 Basic research
## 94 Environmental
## 95 Basic research
## 96 Environmental
## 97 Basic research
## 98 Medical
## 99 Medical
## 100 Medical
## 101 Medical
## 102 Medical
## 103 Medical
## 104 Environmental
## 105 Medical
## 106 Basic research
## 107 Basic research
## 108 Basic research
## 109 Basic research
## 110 Basic research
## 111 Basic research
## 112 Basic research
## 113 Environmental
## 114 Basic research
## 115 Basic research
## 116 Basic research
## 117 Basic research
## 118 Basic research
## 119 Medical
## 120 Medical
## 121 Environmental
## 122 Environmental
## 123 Environmental
## 124 Environmental
## 125 Medical
## 126 Medical
## 127 Medical
## 128 Basic research
## 129 Basic research
## 130 Medical
## 131 Medical
## 132 Environmental
## 133 Environmental
## 134 Environmental
## 135 Environmental
## 136 Medical
## 137 Medical
## 138 Environmental
## 139 Environmental
## 140 Environmental
## 141 Environmental
## 142 Environmental
## 143 Environmental
## 144 Medical
## 145 Medical
## 146 Medical
## 147 Medical
## 148 Medical
## 149 Basic research
## 150 Basic research
## 151 Basic research
## 152 Basic research
## 153 Medical
## 154 Medical
## 155 Medical
## 156 Medical
## 157 Medical
## 158 Medical
## 159 Medical
## 160 Environmental
## 161 Basic research
## 162 Medical
## 163 Environmental
## 164 Medical
## 165 Medical
## 166 Environmental
## 167 Environmental
## 168 Environmental
## 169 Environmental
## 170 Medical
## 171 Medical
## 172 Environmental
## 173 Environmental
## 174 Environmental
## 175 Environmental
## 176 Environmental
## 177 Environmental
## 178 Medical
## 179 Medical
## 180 Medical
## 181 Medical
## 182 Medical
## 183 Medical
## 184 Environmental
## 185 Medical
## 186 Medical
## 187 Medical
## 188 Basic research
## 189 Environmental
## 190 Medical
## 191 Medical
## 192 Medical
## 193 Medical
## 194 Medical
## 195 Medical
## 196 Medical
## 197 Medical
## 198 Environmental
## 199 Environmental
## 200 Environmental
## 201 Environmental
## 202 Environmental
## 203 Environmental
## 204 Environmental
## 205 Environmental
## 206 Environmental
## 207 Environmental
## 208 Environmental
## 209 Environmental
## 210 Environmental
## 211 Environmental
## 212 Environmental
## 213 Environmental
## 214 Environmental
## 215 Environmental
## 216 Environmental
## 217 Environmental
## 218 Environmental
## 219 Environmental
## 220 Environmental
## 221 Environmental
## 222 Environmental
## 223 Environmental
## 224 Medical
## 225 Medical
## 226 Medical
## 227 Medical
## 228 Medical
## 229 Medical
## 230 Medical
## 231 Environmental
## 232 Environmental
## 233 Environmental
## 234 Environmental
## 235 Environmental
## 236 Environmental
## 237 Medical
## 238 Medical
## 239 Medical
## 240 Environmental
## 241 Environmental
## 242 Environmental
## 243 Environmental
## 244 Environmental
## 245 Environmental
## 246 Environmental
## 247 Environmental
## 248 Medical
## 249 Medical
## 250 Environmental
## 251 Basic research
## 252 Basic research
## 253 Environmental
## 254 Environmental
## 255 Environmental
## 256 Environmental
## 257 Environmental
## 258 Environmental
## 259 Environmental
## 260 Environmental
## 261 Environmental
## 262 Environmental
## 263 Environmental
## 264 Environmental
## 265 Medical
## 266 Environmental
## 267 Basic research
## 268 Environmental
## 269 Environmental
## 270 Environmental
## 271 Environmental
## 272 Basic research
## 273 Medical
## 274 Basic research
## 275 Basic research
## 276 Basic research
## 277 Medical
## 278 Medical
## 279 Medical
## 280 Medical
## 281 Medical
## 282 Environmental
## 283 Medical
## 284 Medical
## 285 Environmental
## 286 Medical
## 287 Medical
## 288 Medical
## 289 Environmental
## 290 Environmental
## 291 Basic research
## 292 Basic research
## 293 Basic research
## 294 Basic research
## 295 Basic research
## 296 Basic research
## 297 Basic research
## 298 Medical
## 299 Environmental
## 300 Medical
## 301 Medical
## 302 Medical
## 303 Medical
## 304 Environmental
## 305 Environmental
## 306 Environmental
## 307 Basic research
## 308 Environmental
## 309 Environmental
## 310 Environmental
## 311 Medical
## 312 Medical
## 313 Medical
## 314 Medical
## 315 Medical
## 316 Environmental
## 317 Basic research
## 318 Basic research
## 319 Basic research
## 320 Basic research
## 321 Basic research
## 322 Basic research
## 323 Basic research
## 324 Basic research
## 325 Basic research
## 326 Basic research
## 327 Basic research
## 328 Basic research
## 329 Basic research
## 330 Basic research
## 331 Basic research
## 332 Basic research
## 333 Basic research
## 334 Basic research
## 335 Basic research
## 336 Basic research
## 337 Basic research
## 338 Basic research
## 339 Basic research
## 340 Basic research
## 341 Basic research
## 342 Basic research
## 343 Environmental
## 344 Environmental
## 345 Basic research
## 346 Environmental
## 347 Medical
## 348 Medical
## 349 Medical
## 350 Medical
## 351 Medical
## 352 Environmental
## 353 Basic research
## 354 Basic research
## 355 Medical
## 356 Medical
## 357 Medical
## 358 Medical
## 359 Environmental
## 360 Medical
## 361 Medical
## 362 Basic research
## 363 Environmental
## 364 Environmental
## 365 Environmental
## 366 Environmental
## 367 Environmental
## 368 Environmental
## 369 Environmental
## 370 Environmental
## 371 Environmental
## 372 Environmental
## 373 Environmental
## 374 Environmental
## 375 Environmental
## 376 Environmental
## 377 Environmental
## 378 Environmental
## 379 Environmental
## 380 Medical
## 381 Medical
## 382 Medical
## 383 Basic research
## 384 Basic research
## 385 Basic research
## 386 Medical
## 387 Basic research
## 388 Environmental
## 389 Environmental
## 390 Basic research
## 391 Basic research
## 392 Basic research
## 393 Environmental
## 394 Environmental
## 395 Medical
## 396 Medical
## 397 Medical
## 398 Medical
## 399 Medical
## 400 Basic research
## 401 Basic research
## 402 Environmental
## 403 Environmental
## 404 Environmental
## 405 Environmental
## 406 Environmental
## 407 Environmental
## 408 Medical
## 409 Environmental
## 410 Environmental
## 411 Environmental
## 412 Environmental
## 413 Medical
## 414 Medical
## 415 Environmental
## 416 Environmental
## 417 Basic research
## 418 Environmental
## 419 Environmental
## 420 Environmental
## 421 Environmental
## 422 Environmental
## 423 Medical
## 424 Medical
## 425 Medical
## 426 Medical
## 427 Environmental
## 428 Environmental
## 429 Basic research
## 430 Basic research
## 431 Basic research
## 432 Basic research
## 433 Environmental
## 434 Environmental
## 435 Environmental
## 436 Medical
## 437 Medical
## 438 Medical
## 439 Medical
## 440 Medical
## 441 Medical
## 442 Medical
## 443 Medical
## 444 Environmental
## 445 Medical
## 446 Environmental
## 447 Environmental
## 448 Environmental
## 449 Environmental
## 450 Environmental
## 451 Environmental
## 452 Medical
## 453 Environmental
## 454 Environmental
## 455 Environmental
## 456 Medical
## 457 Medical
## 458 Medical
## 459 Medical
## 460 Medical
## 461 Medical
## 462 Environmental
## 463 Medical
## 464 Basic research
## 465 Basic research
## 466 Environmental
## 467 Environmental
## 468 Environmental
## 469 Environmental
## 470 Medical
## 471 Basic research
## 472 Basic research
## 473 Medical
## 474 Medical
## 475 Medical
## 476 Environmental
## 477 Medical
## 478 Environmental
## 479 Environmental
## 480 Environmental
## 481 Environmental
## 482 Environmental
## 483 Environmental
## 484 Basic research
## 485 Basic research
## 486 Environmental
## 487 Basic research
## 488 Environmental
## 489 Environmental
## 490 Environmental
## 491 Environmental
## 492 Basic research
## 493 Environmental
## 494 Environmental
## 495 Environmental
## 496 Basic research
## 497 Basic research
## 498 Basic research
## 499 Basic research
## 500 Basic research
## 501 Basic research
## 502 Basic research
## 503 Basic research
## 504 Basic research
## 505 Environmental
## 506 Basic research
## 507 Basic research
## 508 Environmental
## 509 Medical
## 510 Medical
## 511 Medical
## 512 Medical
## 513 Medical
## 514 Medical
## 515 Medical
## 516 Medical
## 517 Medical
## 518 Medical
## 519 Medical
## 520 Medical
## 521 Medical
## 522 Medical
## 523 Medical
## 524 Medical
## 525 Medical
## 526 Medical
## 527 Medical
## 528 Medical
## 529 Medical
## 530 Medical
## 531 Medical
## 532 Medical
## 533 Medical
## 534 Environmental
## 535 Environmental
## 536 Environmental
## 537 Environmental
## 538 Environmental
## 539 Environmental
## 540 Environmental
## 541 Environmental
## 542 Basic research
## 543 Basic research
## 544 Basic research
## 545 Basic research
## 546 Environmental
## 547 Environmental
## 548 Basic research
## 549 Medical
## 550 Medical
## 551 Medical
## 552 Environmental
## 553 Environmental
## 554 Environmental
## 555 Basic research
## 556 Environmental
## 557 Basic research
## 558 Environmental
## 559 Environmental
## 560 Medical
## 561 Basic research
## 562 Medical
## 563 Medical
## 564 Environmental
## 565 Environmental
## 566 Environmental
## 567 Environmental
## 568 Environmental
## 569 Environmental
## 570 Environmental
## 571 Medical
## 572 Basic research
## 573 Basic research
## 574 Environmental
## 575 Environmental
## 576 Basic research
## 577 Basic research
## 578 Environmental
## 579 Environmental
## 580 Environmental
## 581 Environmental
## 582 Environmental
## 583 Environmental
## 584 Environmental
## 585 Environmental
## 586 Basic research
## 587 Basic research
## 588 Environmental
## 589 Environmental
## 590 Environmental
## 591 Environmental
## 592 Environmental
## 593 Environmental
## 594 Basic research
## 595 Basic research
## 596 Basic research
## 597 Basic research
## 598 Basic research
## 599 Environmental
## 600 Environmental
## 601 Medical
## 602 Medical
## 603 Medical
## 604 Medical
## 605 Environmental
## 606 Environmental
## 607 Environmental
## 608 Environmental
## 609 Environmental
## 610 Environmental
## 611 Environmental
## 612 Medical
## 613 Medical
## 614 Environmental
## 615 Environmental
## 616 Medical
## 617 Environmental
## 618 Environmental
## 619 Medical
## 620 Basic research
## 621 Environmental
## 622 Environmental
## 623 Environmental
## 624 Environmental
## 625 Basic research
## 626 Environmental
## 627 Environmental
## 628 Environmental
## 629 Environmental
## 630 Environmental
## 631 Environmental
## 632 Environmental
## 633 Environmental
## 634 Environmental
## 635 Environmental
## 636 Environmental
## 637 Environmental
## 638 Environmental
## 639 Environmental
## 640 Environmental
## 641 Environmental
## 642 Environmental
## 643 Environmental
## 644 Environmental
## 645 Environmental
## 646 Medical
## 647 Medical
## 648 Medical
## 649 Medical
## 650 Medical
## 651 Medical
## 652 Basic research
## 653 Basic research
## 654 Environmental
## 655 Basic research
## 656 Basic research
## 657 Basic research
## 658 Environmental
## 659 Medical
## 660 Medical
## 661 Medical
## 662 Medical
## 663 Medical
## 664 Medical
## 665 Medical
## 666 Medical
## 667 Medical
## 668 Medical
## 669 Medical
## 670 Medical
## 671 Medical
## 672 Medical
## 673 Medical
## 674 Medical
## 675 Medical
## 676 Medical
## 677 Environmental
## 678 Basic research
## 679 Basic research
## 680 Environmental
## 681 Basic research
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## 715 Medical
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## 721 Environmental
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## 778 Environmental
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## 781 Environmental
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## 801 Medical
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## 815 Medical
## 816 Environmental
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## 1740 Basic research
## compound_name
## 1 Buspirone
## 2 Fluoxetine
## 3 Bromazepam
## 4 Clonazepam
## 5 Fluoxetine
## 6 Buspirone
## 7 Escitalopram
## 8 Arecoline
## 9 Alpha-pyrrolidinovalerophenone
## 10 Venlafaxine
## 11 Estrone
## 12 Sertraline
## 13 Sertraline
## 14 Diazepam
## 15 Fluoxetine
## 16 Cannabidiol
## 17 17-alpha-ethinylestradiol
## 18 Melatonin
## 19 Valproate
## 20 Ciprofloxacin
## 21 Fluoxetine
## 22 4-hydroxyandrostenedione
## 23 Cocaine
## 24 Sulpiride
## 25 Diazepam
## 26 17-alpha-ethinylestradiol
## 27 5-fluorouracil
## 28 Leucovorin
## 29 Valproate
## 30 Haloperidol
## 31 Aripiprazole
## 32 Clozapine
## 33 Olanzapine
## 34 Risperidone
## 35 Buspirone
## 36 Alprazolam
## 37 Bupropion
## 38 Duloxetine
## 39 Escitalopram
## 40 Imipramine
## 41 Tianeptine
## 42 Diazepam
## 43 17-beta-trenbolone
## 44 17-alpha-ethinylestradiol
## 45 Dextroamphetamine
## 46 Fluoxetine
## 47 Diazepam
## 48 Chlordiazepoxide
## 49 Clonazepam
## 50 Buspirone
## 51 Moclobemide
## 52 Caffeine
## 53 Scopolamine
## 54 17-alpha-ethinylestradiol
## 55 Diethylstilbestrol
## 56 17-alpha-ethinylestradiol
## 57 Naproxen
## 58 Fluoxetine
## 59 Fluoxetine
## 60 Neostigmine
## 61 Pyridostigmine
## 62 Thrimethoprim
## 63 Clotrimazole
## 64 17-alpha-ethinylestradiol
## 65 17-alpha-ethinylestradiol
## 66 Buspirone
## 67 Nicotine
## 68 Fluoxetine
## 69 Prednisolone
## 70 17-beta-estradiol
## 71 Imipramine
## 72 Ketamine
## 73 Ivermectin
## 74 Estrone
## 75 17-beta-estradiol
## 76 17-alpha-ethinylestradiol
## 77 Fluoxetine
## 78 Fluoxetine
## 79 Sertraline
## 80 Levonorgestrel
## 81 Caffeine
## 82 Arecoline
## 83 Metformin
## 84 Cisplatin
## 85 Methylphenidate
## 86 Neostigmine
## 87 Pyridostigmine
## 88 Fluoxetine
## 89 17-alpha-ethinylestradiol
## 90 Caffeine
## 91 17-alpha-ethinylestradiol
## 92 Valproate
## 93 Caffeine
## 94 Flutamide
## 95 Trazodone
## 96 Oxazepam
## 97 Fluoxetine
## 98 Methamphetamine
## 99 Morphine
## 100 Tramadol
## 101 Fentanyl
## 102 3-methylfentanyl
## 103 Butyrfentanyl
## 104 17-alpha-ethinylestradiol
## 105 Paracetamol
## 106 Fluoxetine
## 107 Diazepam
## 108 Buspirone
## 109 Caffeine
## 110 Diazepam
## 111 Buspirone
## 112 Caffeine
## 113 Bupropion
## 114 Clonazepam
## 115 Bromazepam
## 116 Diazepam
## 117 Buspirone
## 118 Propranolol
## 119 Phenytoin
## 120 Tranylcypromine
## 121 17-alpha-ethinylestradiol
## 122 17-alpha-ethinylestradiol
## 123 Ciprofloxacin
## 124 17-beta-estradiol
## 125 Clemizole
## 126 Diazepam
## 127 Caffeine
## 128 Diazepam
## 129 Desmethyldiazepam
## 130 Scopolamine
## 131 Deprenyl
## 132 Paracetamol
## 133 Ciprofloxacin
## 134 Fluoxetine
## 135 17-alpha-ethinylestradiol
## 136 Cocaine
## 137 Diazepam
## 138 Estrone
## 139 Oxazepam
## 140 Diphenhydramine
## 141 Diazepam
## 142 Doxapram
## 143 Nicotine
## 144 Ketotifen
## 145 Doxylamine
## 146 Dimethindene
## 147 Cetirizine
## 148 Levocabastine
## 149 Citalopram
## 150 Venlafaxine
## 151 Diclofenac
## 152 Naproxen
## 153 Carbamazepine
## 154 Valproate
## 155 Lidocaine
## 156 Haloperidol
## 157 Sulpiride
## 158 Honokiol
## 159 Baclofen
## 160 Carbamazepine
## 161 Aripiprazole
## 162 Nicotine
## 163 Nitrofurantoin
## 164 Prazosin
## 165 Diazepam
## 166 Gemfibrozil
## 167 Genistein
## 168 Daidzein
## 169 Oxazepam
## 170 Mescaline
## 171 Phencyclidine
## 172 Diazepam
## 173 Carbamazepine
## 174 Phenytoin
## 175 17-alpha-ethinylestradiol
## 176 Flutamide
## 177 Ketamine
## 178 Risperidone
## 179 Haloperidol
## 180 Valproate
## 181 Nicotine
## 182 Buprenorphine
## 183 Naloxone
## 184 17-alpha-ethinylestradiol
## 185 25-dimethoxy-4-bromoamphetamine
## 186 Para-methoxyamphetamine
## 187 34-methylenedioxymethamphetamine
## 188 Tofisopam
## 189 Venlafaxine
## 190 Granisetron
## 191 Pizotifen
## 192 Methysergide
## 193 Fluoxetine
## 194 Cyproheptadine
## 195 Flumazenil
## 196 Desipramine
## 197 Diazepam
## 198 Ibuprofen
## 199 Propranolol
## 200 17-alpha-ethinylestradiol
## 201 Albendazole
## 202 Febantel
## 203 Fenbendazole
## 204 Oxfendazole
## 205 Doramectin
## 206 Ivermectin
## 207 Flumethrin
## 208 Toltrazuril
## 209 Acetylsalicylic acid
## 210 Ibuprofen
## 211 Naproxen
## 212 Bezafibrate
## 213 Clofibrate
## 214 Gemfibrozil
## 215 Fluoxetine
## 216 Citalopram
## 217 Venlafaxine
## 218 Amitriptyline
## 219 Sertraline
## 220 Vortioxetine
## 221 Paroxetine
## 222 Fluvoxamine
## 223 17-alpha-ethinylestradiol
## 224 Pentobarbital
## 225 Morphine
## 226 Naloxone
## 227 Fluoxetine
## 228 Lysergic acid diethylamide
## 229 Cocaine
## 230 Tranylcypromine
## 231 Fluoxetine
## 232 Fluoxetine
## 233 Sertraline
## 234 Venlafaxine
## 235 Bupropion
## 236 Fluoxetine
## 237 Tetrahydrocannabinol
## 238 N-methyl-d-aspartate
## 239 Sulpiride
## 240 Doramectin
## 241 Flumethrin
## 242 Toltrazuril
## 243 Fenbendazole
## 244 Sulfaclozine
## 245 Pyrantel pamoate
## 246 Oxazepam
## 247 Cefalexin
## 248 Cocaine
## 249 Lidocaine
## 250 17-alpha-ethinylestradiol
## 251 Tricaine mesylate
## 252 Eugenol
## 253 Nicotine
## 254 Pilocarpine
## 255 Scopolamine
## 256 Mecamylamine
## 257 Deprenyl
## 258 Fluoxetine
## 259 Apomorphine
## 260 Cimetidine
## 261 Diphenhydramine
## 262 Memantine
## 263 Diazepam
## 264 Fluoxetine
## 265 Ketamine
## 266 17-alpha-ethinylestradiol
## 267 Flumazenil
## 268 17-beta-trenbolone
## 269 17-beta-trenbolone
## 270 Fluoxetine
## 271 Sertraline
## 272 Fluoxetine
## 273 Methylphenidate
## 274 Buspirone
## 275 Chlordiazepoxide
## 276 Caffeine
## 277 Carbamazepine
## 278 Fluoxetine
## 279 Venlafaxine
## 280 Phenytoin
## 281 Desvenlafaxine
## 282 17-alpha-ethinylestradiol
## 283 Caffeine
## 284 Diazepam
## 285 Carbamazepine
## 286 Morphine
## 287 Diazepam
## 288 Naloxone
## 289 Fluoxetine
## 290 Citalopram
## 291 Diazepam
## 292 Clonazepam
## 293 Lorazepam
## 294 Triazolam
## 295 Zolpidem
## 296 Midazolam
## 297 Flumazenil
## 298 Ketamine
## 299 Oxazepam
## 300 Fluoxetine
## 301 Imipramine
## 302 Diazepam
## 303 Clonazepam
## 304 Fluoxetine
## 305 Propranolol
## 306 Amitriptyline
## 307 Eugenol
## 308 Tramadol
## 309 Carbamazepine
## 310 Fluoxetine
## 311 Doxepin
## 312 Fluoxetine
## 313 Duloxetine
## 314 Bupropion
## 315 Sertraline
## 316 Ketamine
## 317 Amitriptyline
## 318 34-methylenedioxymethamphetamine
## 319 25-dimethoxy-4-bromoamphetamine
## 320 Para-methoxyamphetamine
## 321 Citalopram
## 322 Clonidine
## 323 Guanfacine
## 324 Yohimbine
## 325 4-hydroxybutyric acid
## 326 Gaboxadol
## 327 Baclofen
## 328 Baclofen
## 329 2-hydroxysaclofen
## 330 Flurazepam
## 331 Diazepam
## 332 Pentobarbital
## 333 Flumazenil
## 334 Chlorpheniramine
## 335 Diphenhydramine
## 336 Mepyramine
## 337 Doxepin
## 338 Gabapentin
## 339 Mephedrone
## 340 Mephedrone
## 341 Mephedrone
## 342 Cocaine
## 343 Oxazepam
## 344 Venlafaxine
## 345 Caffeine
## 346 Fluoxetine
## 347 N-acetylcysteine
## 348 Fluoxetine
## 349 Bromazepam
## 350 Eugenol
## 351 Propofol
## 352 Sertraline
## 353 Fluoxetine
## 354 Morphine
## 355 25-dimethoxy-4-bromoamphetamine
## 356 Para-methoxyamphetamine
## 357 34-methylenedioxymethamphetamine
## 358 Fluoxetine
## 359 Fluoxetine
## 360 Lysergic acid diethylamide
## 361 O-acetyl-l-carnitine hydrochloride
## 362 Betaine
## 363 Diclofenac
## 364 Carbamazepine
## 365 Paracetamol
## 366 Propranolol
## 367 Amitriptyline
## 368 Fluoxetine
## 369 Citalopram
## 370 Sertraline
## 371 Paroxetine
## 372 Bupropion
## 373 Venlafaxine
## 374 Desvenlafaxine
## 375 Carbamazepine
## 376 Oxazepam
## 377 Tramadol
## 378 Haloperidol
## 379 Risperidone
## 380 Chlorpromazine
## 381 Haloperidol
## 382 Clozapine
## 383 Buspirone
## 384 Diazepam
## 385 Chlordiazepoxide
## 386 Fluoxetine
## 387 Citalopram
## 388 17-alpha-ethinylestradiol
## 389 17-beta-trenbolone
## 390 Lidocaine
## 391 Carprofen
## 392 Buprenorphine
## 393 Fluoxetine
## 394 Fluoxetine
## 395 Haloperidol
## 396 Olanzapine
## 397 Sulpiride
## 398 Caffeine
## 399 Cannabidiol
## 400 Diazepam
## 401 Flumazenil
## 402 Venlafaxine
## 403 17-beta-estradiol
## 404 17-beta-estradiol
## 405 Methamphetamine
## 406 Sertraline
## 407 Emamectin benzoate
## 408 Valproate
## 409 Carbamazepine
## 410 17-alpha-ethinylestradiol
## 411 Fluoxetine
## 412 Diltiazem
## 413 Fluoxetine
## 414 Citalopram
## 415 17-alpha-ethinylestradiol
## 416 Oxazepam
## 417 Buspirone
## 418 Fluoxetine
## 419 Fluvoxamine
## 420 Paroxetine
## 421 Sertraline
## 422 17-beta-trenbolone
## 423 Caffeine
## 424 Fluoxetine
## 425 Morphine
## 426 Nicotine
## 427 17-alpha-ethinylestradiol
## 428 17-alpha-ethinylestradiol
## 429 Acetylsalicylic acid
## 430 Morphine
## 431 Lidocaine
## 432 Flunixin
## 433 Hydroxyzine
## 434 Fexofenadine
## 435 17-alpha-ethinylestradiol
## 436 Diazepam
## 437 Fluoxetine
## 438 Caffeine
## 439 Indomethacin
## 440 Donepezil
## 441 Tricaine mesylate
## 442 Ketamine
## 443 Propofol
## 444 Fluoxetine
## 445 Valproate
## 446 Carbamazepine
## 447 Carbamazepine
## 448 Valproate
## 449 Caffeine
## 450 Theophylline
## 451 Pentobarbital
## 452 Reserpine
## 453 Carbamazepine
## 454 Diclofenac
## 455 Oxazepam
## 456 Ketamine
## 457 Triazolam
## 458 Zolpidem
## 459 Methylphenidate
## 460 Modafinil
## 461 Pemoline
## 462 Venlafaxine
## 463 Nicotine
## 464 Fluoxetine
## 465 Chlordiazepoxide
## 466 Amitriptyline
## 467 Iopamidol
## 468 Sertraline
## 469 Methamphetamine
## 470 Alprazolam
## 471 Fluoxetine
## 472 Morphine
## 473 Fluoxetine
## 474 Venlafaxine
## 475 Carbamazepine
## 476 Diltiazem
## 477 Melatonin
## 478 Moxifloxacin
## 479 Rosuvastatin
## 480 Fluoxetine
## 481 Methamphetamine
## 482 17-alpha-ethinylestradiol
## 483 Diazepam
## 484 Thyroxine
## 485 Thyroxine
## 486 Gemfibrozil
## 487 Fluoxetine
## 488 Fluoxetine
## 489 Carbamazepine
## 490 Diclofenac
## 491 Carbamazepine
## 492 Tiletamine
## 493 Diphenhydramine
## 494 17-alpha-ethinylestradiol
## 495 17-alpha-ethinylestradiol
## 496 Morphine
## 497 Caffeine
## 498 Fluoxetine
## 499 Fluoxetine
## 500 Propranolol
## 501 Cyproheptadine
## 502 Mianserin
## 503 Paroxetine
## 504 Fluvoxamine
## 505 Midostaurin
## 506 Scopolamine
## 507 Sulpiride
## 508 Citalopram
## 509 Nicotine
## 510 Mecamylamine
## 511 Bupropion
## 512 Varenicline
## 513 Disulfiram
## 514 Apomorphine
## 515 Betaxolol
## 516 Carisoprodol
## 517 Clonazepam
## 518 Diazepam
## 519 Lorazepam
## 520 Topiramate
## 521 Zolpidem
## 522 Baclofen
## 523 Buspirone
## 524 Citalopram
## 525 Clemastine fumarate
## 526 Deprenyl
## 527 Diltiazem
## 528 Fluoxetine
## 529 Nefazodone
## 530 Olanzapine
## 531 Venlafaxine
## 532 Tiagabine
## 533 Modafinil
## 534 Fluoxetine
## 535 Paracetamol
## 536 Fluoxetine
## 537 Carbamazepine
## 538 Venlafaxine
## 539 Sertraline
## 540 Citalopram
## 541 Fluoxetine
## 542 Flunixin
## 543 Morphine
## 544 Bupivacaine
## 545 Lidocaine
## 546 Fluoxetine
## 547 Fluvoxamine
## 548 20-hydroxyecdysone
## 549 Methylphenidate
## 550 Caffeine
## 551 Morphine
## 552 Fluoxetine
## 553 Tramadol
## 554 Gabapentin
## 555 Muscimol
## 556 Carbamazepine
## 557 Morphine
## 558 Fluoxetine
## 559 Levonorgestrel
## 560 Amitriptyline
## 561 Fluoxetine
## 562 Buspirone
## 563 Ketanserin
## 564 Fluoxetine
## 565 Ibuprofen
## 566 Tamoxifen
## 567 Fulvestrant
## 568 Sertraline
## 569 Venlafaxine
## 570 17-beta-trenbolone
## 571 Fluoxetine
## 572 Arecoline
## 573 Caffeine
## 574 17-alpha-ethinylestradiol
## 575 Sertraline
## 576 Fluoxetine
## 577 Nicotine
## 578 Fluoxetine
## 579 Risperidone
## 580 Ibuprofen
## 581 Diclofenac
## 582 Paracetamol
## 583 Oxytetracycline
## 584 Venlafaxine
## 585 17-beta-estradiol
## 586 Physostigmine
## 587 Scopolamine
## 588 Cocaine
## 589 Sulfamethoxazole
## 590 Sulfadiazine
## 591 Sulfamethazine
## 592 Fluoxetine
## 593 Sertraline
## 594 Mianserin
## 595 Fluoxetine
## 596 Spiperone
## 597 Haloperidol
## 598 Alprenolol
## 599 Fluoxetine
## 600 Diazepam
## 601 Lidocaine
## 602 Fluoxetine
## 603 Diazepam
## 604 Flumazenil
## 605 Oxazepam
## 606 Atorvastatin
## 607 Gemfibrozil
## 608 Aripiprazole
## 609 Fluoxetine
## 610 Fluoxetine
## 611 Tramadol
## 612 Tetrahydrocannabinol
## 613 Cannabidiol
## 614 Fluoxetine
## 615 17-alpha-ethinylestradiol
## 616 Morphine
## 617 Citalopram
## 618 Tramadol
## 619 Melatonin
## 620 Hydrocortisone
## 621 Venlafaxine
## 622 Fluoxetine
## 623 Fluoxetine
## 624 Oxazepam
## 625 Amiloride
## 626 17-beta-estradiol
## 627 Teflubenzuron
## 628 Sertraline
## 629 Fluoxetine
## 630 Propranolol
## 631 Oxytetracycline
## 632 Risperidone
## 633 17-alpha-ethinylestradiol
## 634 Diazepam
## 635 Clonazepam
## 636 Diazepam
## 637 Fluoxetine
## 638 Risperidone
## 639 Buspirone
## 640 Gemfibrozil
## 641 Venlafaxine
## 642 Gabapentin
## 643 Thrimethoprim
## 644 Sertraline
## 645 Sertraline
## 646 Carbamazepine
## 647 Lamotrigine
## 648 Levetiracetam
## 649 Phenobarbital
## 650 Phenytoin
## 651 Valproate
## 652 Fluoxetine
## 653 Buspirone
## 654 Citalopram
## 655 Ethosuximide
## 656 Butorphanol
## 657 Morphine
## 658 Fluoxetine
## 659 Amitriptyline
## 660 Amoxapine
## 661 Sertraline
## 662 Doxepin
## 663 Imipramine
## 664 Moclobemide
## 665 Selegiline
## 666 Escitalopram
## 667 Fluoxetine
## 668 Paroxetine
## 669 Duloxetine
## 670 Milnacipran
## 671 Venlafaxine
## 672 Atomoxetine
## 673 Bupropion
## 674 Mianserin
## 675 Mirtazapine
## 676 Trazodone
## 677 Flutamide
## 678 Fluoxetine
## 679 Ropinirole
## 680 Citalopram
## 681 Naloxone
## 682 17-alpha-ethinylestradiol
## 683 17-alpha-ethinylestradiol
## 684 Citalopram
## 685 Finasteride
## 686 Melengestrol acetate
## 687 Citalopram
## 688 Sertraline
## 689 Methamphetamine
## 690 Tramadol
## 691 Oxazepam
## 692 Bromazepam
## 693 Clobazam
## 694 Temazepam
## 695 Methamphetamine
## 696 Lidocaine
## 697 Fluoxetine
## 698 Ibuprofen
## 699 Carbamazepine
## 700 Fluoxetine
## 701 Tramadol
## 702 Fluoxetine
## 703 Haloperidol
## 704 Fluoxetine
## 705 Fluoxetine
## 706 17-alpha-ethinylestradiol
## 707 17-beta-trenbolone
## 708 Scopolamine
## 709 Melatonin
## 710 Venlafaxine
## 711 Fluoxetine
## 712 Sertraline
## 713 Amitriptyline
## 714 Phenazepam
## 715 Mephedrone
## 716 Cyclosporine
## 717 17-alpha-ethinylestradiol
## 718 Atropine
## 719 Scopolamine
## 720 17-alpha-ethinylestradiol
## 721 Norethindrone
## 722 Mirtazapine
## 723 Morphine
## 724 Ibuprofen
## 725 Ketamine
## 726 17-beta-trenbolone
## 727 Fluoxetine
## 728 Fluoxetine
## 729 Fluoxetine
## 730 Naloxone
## 731 N-omega-nitro-l-arginine methyl ester
## 732 Methylene blue
## 733 Ketamine
## 734 Camphor
## 735 Amiloride
## 736 Mefenamic acid
## 737 Morphine
## 738 Fluoxetine
## 739 Fluoxetine
## 740 17-alpha-ethinylestradiol
## 741 Methadone
## 742 Erythromycin
## 743 Ketoconazole
## 744 Oxazepam
## 745 Diphenyl diselenide
## 746 Temazepam
## 747 Irbesartan
## 748 Propranolol
## 749 Caffeine
## 750 Clonazepam
## 751 Diazepam
## 752 Moclobemide
## 753 Fluoxetine
## 754 Buspirone
## 755 Bupropion
## 756 Diethylpropion
## 757 Chlordiazepoxide
## 758 Sodium nitroprusside
## 759 Verapamil
## 760 Nortriptyline
## 761 Ractopamine
## 762 17-alpha-methyldihydrotestosterone
## 763 Atenolol
## 764 Metoprolol
## 765 Labetalol
## 766 Propranolol
## 767 Caffeine
## 768 Liraglutide
## 769 17-beta-estradiol
## 770 Methyltestosterone
## 771 Salicylic acid
## 772 Tramadol
## 773 Methocarbamol
## 774 Temazepam
## 775 Diclofenac
## 776 Morphine
## 777 Naloxone
## 778 Propranolol
## 779 Pyrovalerone
## 780 Guanylurea
## 781 Fluoxetine
## 782 Fluoxetine
## 783 Diazepam
## 784 Fluoxetine
## 785 Fluoxetine
## 786 Oxazepam
## 787 Clonazepam
## 788 Fluoxetine
## 789 Caffeine
## 790 17-alpha-ethinylestradiol
## 791 Medetomidine
## 792 Caffeine
## 793 Dipyridamole
## 794 Sulpiride
## 795 Ketanserin
## 796 Ondansetron
## 797 Caffeine
## 798 Diazepam
## 799 Citalopram
## 800 Tramadol
## 801 Fluoxetine
## 802 Diazepam
## 803 17-alpha-ethinylestradiol
## 804 Diazepam
## 805 Clonazepam
## 806 Diazepam
## 807 Caffeine
## 808 Fluoxetine
## 809 Citalopram
## 810 Melatonin
## 811 Nicotine
## 812 Diazepam
## 813 Picrotoxin
## 814 Sodium pentobarbital
## 815 Fluoxetine
## 816 Ibuprofen
## 817 Diclofenac
## 818 Carbamazepine
## 819 Bezafibrate
## 820 Sulfamethoxazole
## 821 Caffeine
## 822 Clarithromycin
## 823 Fluoxetine
## 824 Benzocaine
## 825 Tricaine mesylate
## 826 Fluoxetine
## 827 Diazepam
## 828 Lorazepam
## 829 Estradiol valerate
## 830 Estradiol benzoate
## 831 Midazolam
## 832 17-beta-estradiol
## 833 Diazepam
## 834 Melatonin
## 835 Fluoxetine
## 836 Propranolol
## 837 Methylphenidate
## 838 Lidocaine
## 839 Prednisone
## 840 Fluoxetine
## 841 Metformin
## 842 Etomidate
## 843 Propofol
## 844 Pentobarbital
## 845 Alphaxalone
## 846 Ketamine
## 847 Dexmedetomidine
## 848 Meta-chlorophenylpiperazine
## 849 Escitalopram
## 850 Melatonin
## 851 Cannabidiol
## 852 Tetrahydrocannabinol
## 853 Cannabidivarin
## 854 Cannabinol
## 855 Fluoxetine
## 856 Cocaine
## 857 Spiradoline
## 858 Fluoxetine
## 859 Fluoxetine
## 860 Caffeine
## 861 Fluoxetine
## 862 Paracetamol
## 863 Propranolol
## 864 Sertraline
## 865 Ibuprofen
## 866 Paracetamol
## 867 Acetylsalicylic acid
## 868 Amoxicillin
## 869 Bendroflumethiazide
## 870 Furosemide
## 871 Atenolol
## 872 Diazepam
## 873 Digoxin
## 874 Amlodipine
## 875 Sertraline
## 876 Diazepam
## 877 Chlorpromazine
## 878 Citalopram
## 879 Cyamemazine
## 880 Fluoxetine
## 881 Oxazepam
## 882 Sertraline
## 883 Valproate
## 884 Diphenhydramine
## 885 Buspirone
## 886 Vincristine
## 887 Bortezomib
## 888 Diclofenac
## 889 Fluoxetine
## 890 Fluoxetine
## 891 Cocaine
## 892 Fluoxetine
## 893 Metformin
## 894 Ibuprofen
## 895 Fluoxetine
## 896 Carbamazepine
## 897 Diazepam
## 898 Propranolol
## 899 Buspirone
## 900 Sertraline
## 901 Proglumide
## 902 17-beta-estradiol
## 903 Naloxone
## 904 Chlordiazepoxide
## 905 Verapamil
## 906 Ciprofloxacin
## 907 Ofloxacin
## 908 Norfloxacin
## 909 Enrofloxacin
## 910 Chlortetracycline
## 911 Doxycycline
## 912 Apomorphine
## 913 Escitalopram
## 914 Dizocilpine
## 915 Tramadol
## 916 Citalopram
## 917 Sumatriptan
## 918 17-alpha-ethinylestradiol
## 919 Diphenhydramine
## 920 Phenytoin
## 921 Carbamazepine
## 922 Gemfibrozil
## 923 Bisoprolol
## 924 Sotalol
## 925 Ranitidine
## 926 Fluoxetine
## 927 17-beta-estradiol
## 928 34-methylenedioxymethamphetamine
## 929 Paracetamol
## 930 Clonazepam
## 931 Buspirone
## 932 Imipramine
## 933 Fluoxetine
## 934 Paroxetine
## 935 Haloperidol
## 936 Risperidone
## 937 Propranolol
## 938 Fluoxetine
## 939 17-alpha-ethinylestradiol
## 940 Oxazepam
## 941 Methamphetamine
## 942 N-acetylcysteine
## 943 Nicotine
## 944 Mecamylamine
## 945 Phenytoin
## 946 Fluoxetine
## 947 Fluoxetine
## 948 Caffeine
## 949 Pilocarpine
## 950 17-beta-trenbolone
## 951 Galantamine
## 952 Imipramine
## 953 Fluoxetine
## 954 Sertraline
## 955 Venlafaxine
## 956 Valproate
## 957 17-beta-estradiol
## 958 Diclofenac
## 959 17-beta-estradiol
## 960 17-alpha-ethinylestradiol
## 961 Testosterone
## 962 Triiodothyronine
## 963 Hydrocortisone
## 964 Mifepristone
## 965 Fadrozole
## 966 Nilutamide
## 967 Flutamide
## 968 Potassium perchlorate
## 969 Paracetamol
## 970 Dihydrotestosterone
## 971 Thyroxine
## 972 Gemfibrozil
## 973 Fluoxetine
## 974 Fluoxetine
## 975 Diazepam
## 976 Carbamazepine
## 977 Propranolol
## 978 Apinac
## 979 Methiopropamine
## 980 Thyroxine
## 981 Triiodothyronine
## 982 17-beta-estradiol
## 983 Fluoxetine
## 984 Fluoxetine
## 985 Fluoxetine
## 986 Reserpine
## 987 Fluoxetine
## 988 Buspirone
## 989 Fluoxetine
## 990 Etonogestrel
## 991 Oxazepam
## 992 Berberine
## 993 Valproate
## 994 Fluoxetine
## 995 Fluoxetine
## 996 Methamphetamine
## 997 Chlordiazepoxide
## 998 Fluoxetine
## 999 Bupropion
## 1000 Fg-7142
## 1001 Mephedrone
## 1002 Bupropion
## 1003 17-beta-trenbolone
## 1004 Isoproterenol
## 1005 Propranolol
## 1006 Phenylephrine
## 1007 Apomorphine
## 1008 Chlorpromazine
## 1009 Atropine
## 1010 Mecamylamine
## 1011 Atenolol
## 1012 Metoprolol
## 1013 Nadolol
## 1014 Acetylcholine
## 1015 Sotalol
## 1016 Diazepam
## 1017 Fgin-1-27
## 1018 Tramadol
## 1019 Dadle
## 1020 U-50488
## 1021 Diazepam
## 1022 Dopamine
## 1023 Midazolam
## 1024 Methamphetamine
## 1025 Tramadol
## 1026 Fluoxetine
## 1027 Propranolol
## 1028 Gemfibrozil
## 1029 Proline
## 1030 Tramadol
## 1031 Nicotine
## 1032 Cotinine
## 1033 Anatabine
## 1034 Nornicotine
## 1035 Metanicotine oxalate
## 1036 Azd1446
## 1037 Acetylcholine
## 1038 Buspirone
## 1039 Citalopram
## 1040 Simvastatin
## 1041 Olanzapine
## 1042 17-beta-estradiol
## 1043 Methamphetamine hydrochloride
## 1044 Nociceptin
## 1045 Jtc-801
## 1046 Valproate
## 1047 Methyl methanesulfonate
## 1048 Acetylsalicylic acid
## 1049 Carbamazepine
## 1050 Diazepam
## 1051 Ethosuximide
## 1052 Gabapentin
## 1053 Lamotrigine
## 1054 Levetiracetam
## 1055 Oxcarbazepine
## 1056 Phenytoin
## 1057 Primidone
## 1058 Tiagabine
## 1059 Topiramate
## 1060 Valproate
## 1061 Zonisamide
## 1062 N-ethylpentylone
## 1063 Sch23390
## 1064 Sulpiride
## 1065 Amphetamine
## 1066 Dizocilpine
## 1067 Sulfamethazine
## 1068 Halothane
## 1069 Isoflurane
## 1070 Sevoflurane
## 1071 Propofol
## 1072 Etomidate
## 1073 Ketamine
## 1074 Dexmedetomidine
## 1075 Carbamazepine
## 1076 Buspirone
## 1077 Dextroamphetamine
## 1078 Carbamazepine
## 1079 Diclofenac
## 1080 17-beta-trenbolone
## 1081 Paracetamol
## 1082 Acetylsalicylic acid
## 1083 Diclofenac
## 1084 Ibuprofen
## 1085 Carbamazepine
## 1086 Acetazolamide
## 1087 Ethoxyzolamide
## 1088 Dorzolamide
## 1089 Erythromycin
## 1090 Ibuprofen
## 1091 Furosemide
## 1092 Thrimethoprim
## 1093 Caffeine
## 1094 Acetic acid
## 1095 Clozapine
## 1096 17-beta-trenbolone
## 1097 Caffeine
## 1098 Fluoxetine
## 1099 Chlordiazepoxide
## 1100 Ketoprofen
## 1101 Morphine
## 1102 Naloxone
## 1103 Caffeine
## 1104 Buspirone
## 1105 Fluoxetine
## 1106 Clarithromycin
## 1107 Florfenicol
## 1108 Sulfamethazine
## 1109 17-alpha-ethinylestradiol
## 1110 17-alpha-ethinylestradiol
## 1111 Progesterone
## 1112 Valproate
## 1113 Carbamazepine
## 1114 Ethosuximide
## 1115 Levetiracetam
## 1116 Flutamide
## 1117 Diazepam
## 1118 Chlorpromazine
## 1119 Diltiazem
## 1120 1234-tetrahydroisoquinoline
## 1121 Pargyline
## 1122 Buspirone
## 1123 Capsaicin
## 1124 Naloxone
## 1125 Ketamine
## 1126 Camphor
## 1127 Amiloride
## 1128 Morphine
## 1129 17-alpha-ethinylestradiol
## 1130 Morphine
## 1131 17-beta-estradiol
## 1132 Valproate
## 1133 Fluoxetine
## 1134 N-acetylcysteine amide
## 1135 Fluoxetine
## 1136 N-acetylcysteine
## 1137 Aripiprazole
## 1138 Risperidone
## 1139 Fluoxetine
## 1140 Clozapine
## 1141 Naltrexone
## 1142 Fluoxetine
## 1143 Sertraline
## 1144 Pizotifen
## 1145 Granisetron
## 1146 Cyproheptadine
## 1147 Diazepam
## 1148 Fluoxetine
## 1149 17-alpha-ethinylestradiol
## 1150 Reserpine
## 1151 Dextroamphetamine
## 1152 Barbiturate
## 1153 Diazepam
## 1154 B-carboline
## 1155 Tricaine mesylate
## 1156 Fadrozole
## 1157 Risperidone
## 1158 8-trifluoromethyl-12345-benzopentathiepin-6-amine hydrochloride
## 1159 Fluoxetine
## 1160 17-alpha-ethinylestradiol
## 1161 Fluoxetine
## 1162 Morphine
## 1163 Ketamine
## 1164 Methamphetamine
## 1165 Lidocaine
## 1166 Butorphanol
## 1167 Acetylsalicylic acid
## 1168 Ketamine
## 1169 Citalopram
## 1170 Paracetamol
## 1171 Caffeine
## 1172 Tetracaine hydrochloride
## 1173 Lidocaine
## 1174 Bupivacaine
## 1175 Fluoxetine
## 1176 Oxazepam
## 1177 Benzoylecgonine
## 1178 17-alpha-ethinylestradiol
## 1179 Imatinib
## 1180 Noribogaine
## 1181 Methyltestosterone
## 1182 Mephedrone
## 1183 Methylone
## 1184 Para-methoxyamphetamine
## 1185 34-methylenedioxymethamphetamine
## 1186 17-alpha-ethinylestradiol
## 1187 Morphine
## 1188 Flunixin
## 1189 Carprofen
## 1190 Fluoxetine
## 1191 1-dodecanol
## 1192 Glycerol
## 1193 12-propanediol
## 1194 Etomidate
## 1195 Valproate
## 1196 Oxazepam
## 1197 Chlorpromazine
## 1198 Morphine
## 1199 Tetrahydrocannabinol
## 1200 1-n-pentyl-3-(1-naphthoyl)indole
## 1201 Nicotine
## 1202 Progesterone
## 1203 Levonorgestrel
## 1204 Methylphenidate
## 1205 Diazepam
## 1206 Fluoxetine
## 1207 Methylphenidate
## 1208 Fluoxetine
## 1209 Metyrapone
## 1210 Citalopram
## 1211 Bromazepam
## 1212 Fluoxetine
## 1213 Nortriptyline
## 1214 Citalopram
## 1215 Fluoxetine
## 1216 Tetrahydrocannabinol
## 1217 17-alpha-ethinylestradiol
## 1218 17-beta-estradiol
## 1219 Sertraline
## 1220 Simvastatin
## 1221 Metformin
## 1222 Diazepam
## 1223 Omeprazole
## 1224 Ractopamine hydrochloride
## 1225 Methylphenidate
## 1226 17-beta-trenbolone
## 1227 Methylphenidate
## 1228 17-alpha-ethinylestradiol
## 1229 17-beta-estradiol
## 1230 17-alpha-ethinylestradiol
## 1231 Ceftriaxone
## 1232 Valproate
## 1233 Carbamazepine
## 1234 Venlafaxine
## 1235 Isoproterenol
## 1236 Apomorphine
## 1237 Chlordiazepoxide
## 1238 Fluoxetine
## 1239 Ivermectin
## 1240 Histamine
## 1241 Ketoprofen
## 1242 Venlafaxine
## 1243 Physostigmine
## 1244 Nicotine
## 1245 Diazepam
## 1246 Flumazenil
## 1247 Clozapine
## 1248 Fluoxetine
## 1249 Diazepam
## 1250 Cocaine
## 1251 Methamphetamine
## 1252 Mephedrone
## 1253 Nicotine
## 1254 Cannabinol
## 1255 Caffeine
## 1256 Fluphenazine
## 1257 Levodopa
## 1258 Haloperidol
## 1259 Olanzapine
## 1260 17-alpha-ethinylestradiol
## 1261 Fluoxetine
## 1262 Sertraline
## 1263 Venlafaxine
## 1264 Diazepam
## 1265 Xanthotoxin
## 1266 Fluoxetine
## 1267 Piracetam
## 1268 Fluoxetine
## 1269 Fluoxetine
## 1270 Ciprofloxacin
## 1271 Lithium carbonate
## 1272 Isoproterenol
## 1273 Gestodene
## 1274 Ivermectin
## 1275 Diazepam
## 1276 17-beta-estradiol
## 1277 17-beta-estradiol
## 1278 Gemfibrozil
## 1279 Clonazepam
## 1280 17-beta-estradiol
## 1281 Progesterone
## 1282 Emamectin benzoate
## 1283 Venlafaxine
## 1284 Buspirone
## 1285 Caffeine
## 1286 Desipramine
## 1287 Fluoxetine
## 1288 Citalopram
## 1289 Fluoxetine
## 1290 Levonorgestrel
## 1291 Paracetamol
## 1292 Diazepam
## 1293 Ipsapirone
## 1294 Lorcaserin
## 1295 Cisapride
## 1296 Carbamazepine
## 1297 Metronidazole
## 1298 Olaquindox
## 1299 Oxolinic acid
## 1300 Oxytetracycline
## 1301 Streptomycin
## 1302 Sulfadiazine
## 1303 Tetracycline
## 1304 Tiamulin
## 1305 Tylosin
## 1306 Tricaine mesylate
## 1307 Oxazepam
## 1308 Nadolol
## 1309 Yohimbine
## 1310 Propranolol
## 1311 Cyproheptadine
## 1312 Ibuprofen
## 1313 Fenoprofen
## 1314 Naproxen
## 1315 Mefenamic acid
## 1316 Ketoprofen
## 1317 Diethyltoluamide
## 1318 Crotamiton
## 1319 Carbamazepine
## 1320 Clarithromycin
## 1321 Azithromycin
## 1322 Apomorphine
## 1323 Bromocriptine
## 1324 Rotigotine
## 1325 Remoxipride
## 1326 Raclopride
## 1327 Sulpiride
## 1328 Norfloxacin
## 1329 Streptomycin
## 1330 Estrone
## 1331 34-methylenedioxymethamphetamine
## 1332 Caffeine
## 1333 17-alpha-ethinylestradiol
## 1334 Fluoxetine
## 1335 Citalopram
## 1336 17-alpha-ethinylestradiol
## 1337 Atomoxetine
## 1338 Methylphenidate
## 1339 Haloperidol
## 1340 Diazepam
## 1341 Flumazenil
## 1342 Zonisamide
## 1343 Levetiracetam
## 1344 Perampanel
## 1345 Lamotrigine
## 1346 Valproate
## 1347 Acetylsalicylic acid
## 1348 Morphine
## 1349 Lidocaine
## 1350 Flunixin
## 1351 Paracetamol
## 1352 Oxazepam
## 1353 17-beta-trenbolone
## 1354 Apomorphine
## 1355 Aminosidine
## 1356 Bacitracin
## 1357 Erythromycin
## 1358 Lincomycin
## 1359 17-alpha-ethinylestradiol
## 1360 Promethazine
## 1361 Levonorgestrel
## 1362 Procaine benzylpenicillin
## 1363 Sertraline
## 1364 Cisplatin
## 1365 Citalopram
## 1366 Desipramine
## 1367 Chlordiazepoxide
## 1368 Yohimbine
## 1369 Nicotine
## 1370 Gemfibrozil
## 1371 Ibuprofen
## 1372 Naproxen
## 1373 Carbamazepine
## 1374 Bezafibrate
## 1375 Sulfapyridine
## 1376 Oxytetracycline
## 1377 Novobiocin
## 1378 Sulfamethoxazole
## 1379 Thrimethoprim
## 1380 Caffeine
## 1381 Nicotine
## 1382 Agomelatine
## 1383 Carbamazepine
## 1384 Lithium
## 1385 Cannabidiol
## 1386 Sildenafil
## 1387 Sertraline
## 1388 Forskolin
## 1389 Haloperidol
## 1390 Caffeine
## 1391 Rimonabant
## 1392 Troglitazone
## 1393 Vorinostat
## 1394 Fenofibrate
## 1395 Simvastatin
## 1396 Guanfacine
## 1397 Trifluoperazine
## 1398 Methylprednisolone
## 1399 Venlafaxine
## 1400 Rolipram
## 1401 Deprenyl
## 1402 Dipyridamole
## 1403 Estradiol benzoate
## 1404 Decitabine
## 1405 Morphine
## 1406 Testosterone
## 1407 Risperidone
## 1408 Ziprasidone
## 1409 Eicosapentaenoic acid
## 1410 Methylphenidate
## 1411 Fluoxetine
## 1412 Rosiglitazone
## 1413 Corticotropin
## 1414 Hydrocortisone
## 1415 Troleandomycin
## 1416 Doxorubicin
## 1417 Ketoconazole
## 1418 Sodium nitroprusside
## 1419 Aminophylline
## 1420 Methylene blue
## 1421 Tolcapone
## 1422 Clonidine
## 1423 Modafinil
## 1424 D-glucose
## 1425 Streptozocin
## 1426 Progesterone
## 1427 Deferoxamine
## 1428 Trazodone
## 1429 Paroxetine
## 1430 Betaxolol
## 1431 17-beta-estradiol
## 1432 Cocaine
## 1433 Ibuprofen
## 1434 Carbamazepine
## 1435 Fluoxetine
## 1436 Oxazepam
## 1437 Flutamide
## 1438 Fluoxetine
## 1439 Betaine
## 1440 2-carboxy-ethyl dimethyl sulphonium bromide
## 1441 Inosinic acid
## 1442 Taurine
## 1443 Cyclophosphamide
## 1444 Venlafaxine
## 1445 Reserpine
## 1446 Fluoxetine
## 1447 Citalopram
## 1448 Apomorphine
## 1449 Fluoxetine
## 1450 Nicotine
## 1451 Buspirone
## 1452 Diazepam
## 1453 Diazepam
## 1454 Reserpine
## 1455 Suvorexant
## 1456 Amitriptyline
## 1457 17-beta-trenbolone
## 1458 Venlafaxine
## 1459 Venlafaxine
## 1460 Cocaine
## 1461 Benzoylecgonine
## 1462 Fluoxetine
## 1463 Fluoxetine
## 1464 Fluoxetine
## 1465 Buspirone
## 1466 Tamoxifen
## 1467 Cetirizine
## 1468 Citalopram
## 1469 Caffeine
## 1470 Atenolol
## 1471 Metoprolol
## 1472 Atorvastatin
## 1473 Bezafibrate
## 1474 Salicylic acid
## 1475 Salicylic acid
## 1476 Prednisolone
## 1477 Fentanyl
## 1478 Fentanyl
## 1479 17-alpha-ethinylestradiol
## 1480 Ibuprofen
## 1481 Venlafaxine
## 1482 Gabapentin
## 1483 Oxazepam
## 1484 Fluoxetine
## 1485 Fluoxetine
## 1486 Opicapone
## 1487 Lamotrigine
## 1488 Reserpine
## 1489 Valproate
## 1490 Caffeine
## 1491 Diazepam
## 1492 Diazepam
## 1493 Flumazenil
## 1494 Buspirone
## 1495 Lidocaine
## 1496 Scopolamine
## 1497 Diazepam
## 1498 Flumazenil
## 1499 Diazepam
## 1500 Clomipramine
## 1501 Paroxetine
## 1502 Trazodone
## 1503 Diazepam
## 1504 Caffeine
## 1505 Fluoxetine
## 1506 17-alpha-ethinylestradiol
## 1507 Citalopram
## 1508 Mirtazapine
## 1509 Genistein
## 1510 Caffeine
## 1511 Dipyridamole
## 1512 Valproate
## 1513 Sertraline
## 1514 Clotrimazole
## 1515 Fluoxetine
## 1516 Citalopram
## 1517 Mirtazapine
## 1518 Delorazepam
## 1519 Amphetamine
## 1520 Nicotine
## 1521 Oxycodone
## 1522 Fluoxetine
## 1523 Valproate
## 1524 Duloxetine
## 1525 Fluoxetine
## 1526 17-beta-estradiol
## 1527 Tetrahydrocannabinol
## 1528 Vortioxetine
## 1529 Haloperidol
## 1530 Amitriptyline
## 1531 Venlafaxine
## 1532 Ibuprofen
## 1533 Paracetamol
## 1534 Probenecid
## 1535 Delorazepam
## 1536 Sulfamethoxazole
## 1537 Pyrazinamide
## 1538 Clenbuterol
## 1539 Caffeine
## 1540 Mianserin
## 1541 Fluoxetine
## 1542 Sertraline
## 1543 Alprazolam
## 1544 Lorazepam
## 1545 Codeine
## 1546 Morphine
## 1547 17-beta-trenbolone
## 1548 Fluoxetine
## 1549 Deferoxamine
## 1550 Roxithromycin
## 1551 Scopolamine
## 1552 Phenytoin
## 1553 Oxytetracycline
## 1554 Caffeine
## 1555 Clozapine
## 1556 Gestodene
## 1557 Amitriptyline
## 1558 Metformin
## 1559 Propranolol
## 1560 Reserpine
## 1561 Diazepam
## 1562 Naltrexone
## 1563 Valproate
## 1564 Scopolamine
## 1565 Diclofenac
## 1566 Carbamazepine
## 1567 Reserpine
## 1568 Norethindrone
## 1569 Diazepam
## 1570 Morphine
## 1571 Naloxone
## 1572 Fluoxetine
## 1573 Fluoxetine
## 1574 Sertraline
## 1575 Paroxetine
## 1576 Venlafaxine
## 1577 Morphine
## 1578 Indomethacin
## 1579 Betaine
## 1580 Haloperidol
## 1581 Buspirone
## 1582 Caffeine
## 1583 Fentanyl
## 1584 Oxytetracycline
## 1585 Ethylestrenol
## 1586 Levonorgestrel
## 1587 Fluoxetine
## 1588 Diazepam
## 1589 Doxepin
## 1590 Diltiazem
## 1591 Fluoxetine
## 1592 Alphaxalone
## 1593 Etomidate
## 1594 Propofol
## 1595 Venlafaxine
## 1596 Citalopram
## 1597 Amiodarone
## 1598 Ramipril
## 1599 Simvastatin
## 1600 Verapamil
## 1601 Diclofenac
## 1602 Tricaine mesylate
## 1603 Diazepam
## 1604 Diclofenac
## 1605 Gabapentin
## 1606 Carbamazepine
## 1607 Ibuprofen
## 1608 Fluoxetine
## 1609 Venlafaxine
## 1610 17-alpha-ethinylestradiol
## 1611 Efavirenz
## 1612 Valproate
## 1613 Scopolamine
## 1614 Imipramine
## 1615 Galantamine
## 1616 Dexamethasone
## 1617 Estrone
## 1618 Estrone
## 1619 Metformin
## 1620 Metformin
## 1621 Fluoxetine
## 1622 Fluoxetine
## 1623 Oxazepam
## 1624 Paracetamol
## 1625 Paracetamol
## 1626 Methamphetamine
## 1627 Methamphetamine
## 1628 Methamphetamine
## 1629 Methamphetamine
## 1630 25c-nbome
## 1631 25c-nbome
## 1632 Carbamazepine
## 1633 Carbamazepine
## 1634 Terbuthylazine
## 1635 Terbuthylazine
## 1636 Clotrimazole
## 1637 Clotrimazole
## 1638 Terbinafine
## 1639 Terbinafine
## 1640 Bisoprolol
## 1641 Bisoprolol
## 1642 Metformin
## 1643 Metformin
## 1644 Ranitidine
## 1645 Ranitidine
## 1646 Sotalol
## 1647 Sotalol
## 1648 Caffeine
## 1649 Caffeine
## 1650 Fluoxetine
## 1651 Fluoxetine
## 1652 Amitriptyline
## 1653 Sertraline
## 1654 Venlafaxine
## 1655 Amitriptyline
## 1656 Amitriptyline
## 1657 Clomipramine
## 1658 Clomipramine
## 1659 Imipramine
## 1660 Imipramine
## 1661 Diclofenac
## 1662 Diclofenac
## 1663 Fluoxetine
## 1664 Fluoxetine
## 1665 Citalopram
## 1666 Citalopram
## 1667 Phenol
## 1668 Phenol
## 1669 Xylazine
## 1670 Xylazine
## 1671 Sertraline
## 1672 Sertraline
## 1673 Fluoxetine
## 1674 Fluoxetine
## 1675 Venlafaxine
## 1676 Venlafaxine
## 1677 Oxazepam
## 1678 Oxazepam
## 1679 17-alpha-ethinylestradiol
## 1680 17-alpha-ethinylestradiol
## 1681 Citalopram
## 1682 Citalopram
## 1683 Citalopram
## 1684 Citalopram
## 1685 Citalopram
## 1686 Fluoxetine
## 1687 Fluoxetine
## 1688 Fluoxetine
## 1689 Fluoxetine
## 1690 Fluoxetine
## 1691 Fluvoxamine
## 1692 Fluvoxamine
## 1693 Fluvoxamine
## 1694 Fluvoxamine
## 1695 Fluvoxamine
## 1696 Venlafaxine
## 1697 Venlafaxine
## 1698 Venlafaxine
## 1699 Venlafaxine
## 1700 Venlafaxine
## 1701 Amitriptyline
## 1702 Buspirone
## 1703 Clomipramine
## 1704 Citalopram
## 1705 Citalopram
## 1706 Venlafaxine
## 1707 Venlafaxine
## 1708 Citalopram
## 1709 Citalopram
## 1710 Fluoxetine
## 1711 Fluoxetine
## 1712 Cyclophosphamide
## 1713 Metomidate
## 1714 Metomidate
## 1715 Fluoxetine
## 1716 Fluoxetine
## 1717 Fluoxetine
## 1718 Fluoxetine
## 1719 Fluoxetine
## 1720 Fluoxetine
## 1721 Fluoxetine
## 1722 Fluoxetine
## 1723 Fluoxetine
## 1724 Fluoxetine
## 1725 Ibuprofen
## 1726 Ibuprofen
## 1727 Dexmedetomidine
## 1728 Clonidine
## 1729 Epinephrine
## 1730 Norepinephrine
## 1731 Ibuprofen
## 1732 Fluoxetine
## 1733 Sertraline
## 1734 Chlorpromazine
## 1735 Midazolam
## 1736 Caffeine
## 1737 Sulfamethoxazole
## 1738 Norfloxacin
## 1739 Butorphanol
## 1740 Ketoprofen
## compound_atc_level_3
## 1 n05b anxiolytics
## 2 n06a antidepressants
## 3 n05b anxiolytics
## 4 n03a antiepileptics
## 5 n06a antidepressants
## 6 n05b anxiolytics
## 7 n06a antidepressants
## 8 <NA>
## 9 <NA>
## 10 n06a antidepressants
## 11 g03c estrogens
## 12 n06a antidepressants
## 13 n06a antidepressants
## 14 n05b anxiolytics
## 15 n06a antidepressants
## 16 n03a antiepileptics
## 17 g03c estrogens;l02a hormones and related agents
## 18 n05c hypnotics and sedatives
## 19 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 20 j01m quinolone antibacterials [dg:dg01550];s02a antiinfectives;s03a antiinfectives;s01a antiinfectives
## 21 n06a antidepressants
## 22 l02b hormone antagonists and related agents
## 23 <NA>
## 24 n05a antipsychotics
## 25 n05b anxiolytics
## 26 g03c estrogens;l02a hormones and related agents
## 27 l01b antimetabolites
## 28 <NA>
## 29 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 30 n05a antipsychotics
## 31 n05a antipsychotics
## 32 n05a antipsychotics
## 33 n05a antipsychotics
## 34 n05a antipsychotics
## 35 n05b anxiolytics
## 36 n05b anxiolytics
## 37 n06a antidepressants
## 38 n06a antidepressants
## 39 n06a antidepressants
## 40 n06a antidepressants
## 41 n06a antidepressants
## 42 n05b anxiolytics
## 43 <NA>
## 44 g03c estrogens;l02a hormones and related agents
## 45 <NA>
## 46 n06a antidepressants
## 47 n05b anxiolytics
## 48 n05b anxiolytics
## 49 n03a antiepileptics
## 50 n05b anxiolytics
## 51 n06a antidepressants
## 52 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 53 a04a antiemetics and antinauseants;n05c hypnotics and sedatives;s01f mydriatics and cycloplegics
## 54 g03c estrogens;l02a hormones and related agents
## 55 g03c estrogens;l02a hormones and related agents
## 56 g03c estrogens;l02a hormones and related agents
## 57 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals
## 58 n06a antidepressants
## 59 n06a antidepressants
## 60 n07a parasympathomimetics;s01e antiglaucoma preparations and miotics
## 61 n07a parasympathomimetics
## 62 j01e sulfonamides and trimethoprim
## 63 a01a stomatological preparations;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d01a antifungals for topical use
## 64 g03c estrogens;l02a hormones and related agents
## 65 g03c estrogens;l02a hormones and related agents
## 66 n05b anxiolytics
## 67 n07b drugs used in addictive disorders
## 68 n06a antidepressants
## 69 a07e intestinal antiinflammatory agents;a01a stomatological preparations;r01a decongestants and other nasal preparations for topical use;d07a corticosteroids, plain;d07x corticosteroids, other combinations;s02b corticosteroids;s03b corticosteroids;s01c antiinflammatory agents and antiinfectives in combination;s01b antiinflammatory agents;h02a corticosteroids for systemic use, plain;c05a agents for treatment of hemorrhoids and anal fissures for topical use
## 70 g03c estrogens;g03c estrogens
## 71 n06a antidepressants
## 72 n01a anesthetics, general
## 73 <NA>
## 74 g03c estrogens
## 75 g03c estrogens;g03c estrogens
## 76 g03c estrogens;l02a hormones and related agents
## 77 n06a antidepressants
## 78 n06a antidepressants
## 79 n06a antidepressants
## 80 g03a hormonal contraceptives for systemic use;g03a hormonal contraceptives for systemic use
## 81 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 82 <NA>
## 83 a10b blood glucose lowering drugs, excl. insulins
## 84 l01x other antineoplastic agents
## 85 n06b psychostimulants, agents used for adhd and nootropics
## 86 n07a parasympathomimetics;s01e antiglaucoma preparations and miotics
## 87 n07a parasympathomimetics
## 88 n06a antidepressants
## 89 g03c estrogens;l02a hormones and related agents
## 90 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 91 g03c estrogens;l02a hormones and related agents
## 92 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 93 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 94 l02b hormone antagonists and related agents
## 95 n06a antidepressants
## 96 n05b anxiolytics
## 97 n06a antidepressants
## 98 n06b psychostimulants, agents used for adhd and nootropics
## 99 n02a opioids
## 100 n02a opioids
## 101 n02a opioids;n01a anesthetics, general
## 102 <NA>
## 103 <NA>
## 104 g03c estrogens;l02a hormones and related agents
## 105 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 106 n06a antidepressants
## 107 n05b anxiolytics
## 108 n05b anxiolytics
## 109 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 110 n05b anxiolytics
## 111 n05b anxiolytics
## 112 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 113 n06a antidepressants
## 114 n03a antiepileptics
## 115 n05b anxiolytics
## 116 n05b anxiolytics
## 117 n05b anxiolytics
## 118 c07a beta blocking agents
## 119 n03a antiepileptics;n03a antiepileptics
## 120 n06a antidepressants
## 121 g03c estrogens;l02a hormones and related agents
## 122 g03c estrogens;l02a hormones and related agents
## 123 j01m quinolone antibacterials [dg:dg01550];s02a antiinfectives;s03a antiinfectives;s01a antiinfectives
## 124 g03c estrogens;g03c estrogens
## 125 <NA>
## 126 n05b anxiolytics
## 127 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 128 n05b anxiolytics
## 129 n05b anxiolytics
## 130 a04a antiemetics and antinauseants;n05c hypnotics and sedatives;s01f mydriatics and cycloplegics
## 131 <NA>
## 132 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 133 j01m quinolone antibacterials [dg:dg01550];s02a antiinfectives;s03a antiinfectives;s01a antiinfectives
## 134 n06a antidepressants
## 135 g03c estrogens;l02a hormones and related agents
## 136 <NA>
## 137 n05b anxiolytics
## 138 g03c estrogens
## 139 n05b anxiolytics
## 140 r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.;r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.
## 141 n05b anxiolytics
## 142 r07a other respiratory system products
## 143 n07b drugs used in addictive disorders
## 144 r06a antihistamines for systemic use;s01g decongestants and antiallergics
## 145 r06a antihistamines for systemic use
## 146 r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.
## 147 r06a antihistamines for systemic use;s01g decongestants and antiallergics
## 148 r01a decongestants and other nasal preparations for topical use;s01g decongestants and antiallergics
## 149 n06a antidepressants
## 150 n06a antidepressants
## 151 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 152 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals
## 153 n03a antiepileptics
## 154 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 155 r02a throat preparations;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s02d other otologicals;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01b antiarrhythmics, class i and iii
## 156 n05a antipsychotics
## 157 n05a antipsychotics
## 158 <NA>
## 159 m03b muscle relaxants, centrally acting agents
## 160 n03a antiepileptics
## 161 n05a antipsychotics
## 162 n07b drugs used in addictive disorders
## 163 j01x other antibacterials
## 164 c02c antiadrenergic agents, peripherally acting
## 165 n05b anxiolytics
## 166 c10a lipid modifying agents, plain
## 167 <NA>
## 168 <NA>
## 169 n05b anxiolytics
## 170 <NA>
## 171 <NA>
## 172 n05b anxiolytics
## 173 n03a antiepileptics
## 174 n03a antiepileptics;n03a antiepileptics
## 175 g03c estrogens;l02a hormones and related agents
## 176 l02b hormone antagonists and related agents
## 177 n01a anesthetics, general
## 178 n05a antipsychotics
## 179 n05a antipsychotics
## 180 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 181 n07b drugs used in addictive disorders
## 182 n07b drugs used in addictive disorders;n02a opioids
## 183 a06a drugs for constipation;v03a all other therapeutic products
## 184 g03c estrogens;l02a hormones and related agents
## 185 <NA>
## 186 <NA>
## 187 <NA>
## 188 n05b anxiolytics
## 189 n06a antidepressants
## 190 a04a antiemetics and antinauseants
## 191 n02c antimigraine preparations
## 192 n02c antimigraine preparations
## 193 n06a antidepressants
## 194 r06a antihistamines for systemic use
## 195 v03a all other therapeutic products
## 196 n06a antidepressants
## 197 n05b anxiolytics
## 198 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 199 c07a beta blocking agents
## 200 g03c estrogens;l02a hormones and related agents
## 201 p02c antinematodal agents
## 202 <NA>
## 203 p02c antinematodal agents
## 204 <NA>
## 205 <NA>
## 206 <NA>
## 207 <NA>
## 208 <NA>
## 209 b01a antithrombotic agents;a01a stomatological preparations;n02b other analgesics and antipyretics
## 210 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 211 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals
## 212 c10a lipid modifying agents, plain
## 213 c10a lipid modifying agents, plain
## 214 c10a lipid modifying agents, plain
## 215 n06a antidepressants
## 216 n06a antidepressants
## 217 n06a antidepressants
## 218 n06a antidepressants
## 219 n06a antidepressants
## 220 n06a antidepressants
## 221 n06a antidepressants
## 222 n06a antidepressants
## 223 g03c estrogens;l02a hormones and related agents
## 224 n05c hypnotics and sedatives
## 225 n02a opioids
## 226 a06a drugs for constipation;v03a all other therapeutic products
## 227 n06a antidepressants
## 228 <NA>
## 229 <NA>
## 230 n06a antidepressants
## 231 n06a antidepressants
## 232 n06a antidepressants
## 233 n06a antidepressants
## 234 n06a antidepressants
## 235 n06a antidepressants
## 236 n06a antidepressants
## 237 a04a antiemetics and antinauseants
## 238 <NA>
## 239 n05a antipsychotics
## 240 <NA>
## 241 <NA>
## 242 <NA>
## 243 p02c antinematodal agents
## 244 <NA>
## 245 p02c antinematodal agents
## 246 n05b anxiolytics
## 247 j01d other beta-lactam antibacterials
## 248 <NA>
## 249 r02a throat preparations;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s02d other otologicals;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01b antiarrhythmics, class i and iii
## 250 g03c estrogens;l02a hormones and related agents
## 251 <NA>
## 252 <NA>
## 253 n07b drugs used in addictive disorders
## 254 n07a parasympathomimetics;s01e antiglaucoma preparations and miotics
## 255 a04a antiemetics and antinauseants;n05c hypnotics and sedatives;s01f mydriatics and cycloplegics
## 256 c02b antiadrenergic agents, ganglion-blocking
## 257 <NA>
## 258 n06a antidepressants
## 259 g04b urologicals;n04b dopaminergic agents
## 260 a02b drugs for peptic ulcer and gastro-oesophageal reflux disease (gord)
## 261 r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.;r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.
## 262 n06d anti-dementia drugs
## 263 n05b anxiolytics
## 264 n06a antidepressants
## 265 n01a anesthetics, general
## 266 g03c estrogens;l02a hormones and related agents
## 267 v03a all other therapeutic products
## 268 <NA>
## 269 <NA>
## 270 n06a antidepressants
## 271 n06a antidepressants
## 272 n06a antidepressants
## 273 n06b psychostimulants, agents used for adhd and nootropics
## 274 n05b anxiolytics
## 275 n05b anxiolytics
## 276 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 277 n03a antiepileptics
## 278 n06a antidepressants
## 279 n06a antidepressants
## 280 n03a antiepileptics;n03a antiepileptics
## 281 n06a antidepressants
## 282 g03c estrogens;l02a hormones and related agents
## 283 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 284 n05b anxiolytics
## 285 n03a antiepileptics
## 286 n02a opioids
## 287 n05b anxiolytics
## 288 a06a drugs for constipation;v03a all other therapeutic products
## 289 n06a antidepressants
## 290 n06a antidepressants
## 291 n05b anxiolytics
## 292 n03a antiepileptics
## 293 n05b anxiolytics
## 294 n05c hypnotics and sedatives
## 295 n05c hypnotics and sedatives
## 296 n05c hypnotics and sedatives
## 297 v03a all other therapeutic products
## 298 n01a anesthetics, general
## 299 n05b anxiolytics
## 300 n06a antidepressants
## 301 n06a antidepressants
## 302 n05b anxiolytics
## 303 n03a antiepileptics
## 304 n06a antidepressants
## 305 c07a beta blocking agents
## 306 n06a antidepressants
## 307 <NA>
## 308 n02a opioids
## 309 n03a antiepileptics
## 310 n06a antidepressants
## 311 d04a antipruritics, incl. antihistamines, anesthetics, etc.;n06a antidepressants
## 312 n06a antidepressants
## 313 n06a antidepressants
## 314 n06a antidepressants
## 315 n06a antidepressants
## 316 n01a anesthetics, general
## 317 n06a antidepressants
## 318 <NA>
## 319 <NA>
## 320 <NA>
## 321 n06a antidepressants
## 322 n02c antimigraine preparations;s01e antiglaucoma preparations and miotics;c02a antiadrenergic agents, centrally acting
## 323 c02a antiadrenergic agents, centrally acting
## 324 g04b urologicals
## 325 <NA>
## 326 <NA>
## 327 m03b muscle relaxants, centrally acting agents
## 328 m03b muscle relaxants, centrally acting agents
## 329 <NA>
## 330 n05c hypnotics and sedatives
## 331 n05b anxiolytics
## 332 n05c hypnotics and sedatives
## 333 v03a all other therapeutic products
## 334 r06a antihistamines for systemic use
## 335 r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.;r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.
## 336 r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.
## 337 d04a antipruritics, incl. antihistamines, anesthetics, etc.;n06a antidepressants
## 338 n02b other analgesics and antipyretics
## 339 <NA>
## 340 <NA>
## 341 <NA>
## 342 <NA>
## 343 n05b anxiolytics
## 344 n06a antidepressants
## 345 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 346 n06a antidepressants
## 347 v03a all other therapeutic products;r05c expectorants, excl. combinations with cough suppressants;s01x other ophthalmologicals
## 348 n06a antidepressants
## 349 n05b anxiolytics
## 350 <NA>
## 351 n01a anesthetics, general
## 352 n06a antidepressants
## 353 n06a antidepressants
## 354 n02a opioids
## 355 <NA>
## 356 <NA>
## 357 <NA>
## 358 n06a antidepressants
## 359 n06a antidepressants
## 360 <NA>
## 361 n06b psychostimulants, agents used for adhd and nootropics
## 362 a16a other alimentary tract and metabolism products
## 363 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 364 n03a antiepileptics
## 365 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 366 c07a beta blocking agents
## 367 n06a antidepressants
## 368 n06a antidepressants
## 369 n06a antidepressants
## 370 n06a antidepressants
## 371 n06a antidepressants
## 372 n06a antidepressants
## 373 n06a antidepressants
## 374 n06a antidepressants
## 375 n03a antiepileptics
## 376 n05b anxiolytics
## 377 n02a opioids
## 378 n05a antipsychotics
## 379 n05a antipsychotics
## 380 n05a antipsychotics
## 381 n05a antipsychotics
## 382 n05a antipsychotics
## 383 n05b anxiolytics
## 384 n05b anxiolytics
## 385 n05b anxiolytics
## 386 n06a antidepressants
## 387 n06a antidepressants
## 388 g03c estrogens;l02a hormones and related agents
## 389 <NA>
## 390 r02a throat preparations;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s02d other otologicals;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01b antiarrhythmics, class i and iii
## 391 <NA>
## 392 n07b drugs used in addictive disorders;n02a opioids
## 393 n06a antidepressants
## 394 n06a antidepressants
## 395 n05a antipsychotics
## 396 n05a antipsychotics
## 397 n05a antipsychotics
## 398 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 399 n03a antiepileptics
## 400 n05b anxiolytics
## 401 v03a all other therapeutic products
## 402 n06a antidepressants
## 403 g03c estrogens;g03c estrogens
## 404 g03c estrogens;g03c estrogens
## 405 n06b psychostimulants, agents used for adhd and nootropics
## 406 n06a antidepressants
## 407 <NA>
## 408 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 409 n03a antiepileptics
## 410 g03c estrogens;l02a hormones and related agents
## 411 n06a antidepressants
## 412 c08d selective calcium channel blockers with direct cardiac effects;c05a agents for treatment of hemorrhoids and anal fissures for topical use
## 413 n06a antidepressants
## 414 n06a antidepressants
## 415 g03c estrogens;l02a hormones and related agents
## 416 n05b anxiolytics
## 417 n05b anxiolytics
## 418 n06a antidepressants
## 419 n06a antidepressants
## 420 n06a antidepressants
## 421 n06a antidepressants
## 422 <NA>
## 423 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 424 n06a antidepressants
## 425 n02a opioids
## 426 n07b drugs used in addictive disorders
## 427 g03c estrogens;l02a hormones and related agents
## 428 g03c estrogens;l02a hormones and related agents
## 429 b01a antithrombotic agents;a01a stomatological preparations;n02b other analgesics and antipyretics
## 430 n02a opioids
## 431 r02a throat preparations;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s02d other otologicals;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01b antiarrhythmics, class i and iii
## 432 <NA>
## 433 n05b anxiolytics
## 434 r06a antihistamines for systemic use
## 435 g03c estrogens;l02a hormones and related agents
## 436 n05b anxiolytics
## 437 n06a antidepressants
## 438 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 439 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;s01b antiinflammatory agents;c01e other cardiac preparations
## 440 n06d anti-dementia drugs
## 441 <NA>
## 442 n01a anesthetics, general
## 443 n01a anesthetics, general
## 444 n06a antidepressants
## 445 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 446 n03a antiepileptics
## 447 n03a antiepileptics
## 448 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 449 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 450 r03d other systemic drugs for obstructive airway diseases
## 451 n05c hypnotics and sedatives
## 452 c02a antiadrenergic agents, centrally acting
## 453 n03a antiepileptics
## 454 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 455 n05b anxiolytics
## 456 n01a anesthetics, general
## 457 n05c hypnotics and sedatives
## 458 n05c hypnotics and sedatives
## 459 n06b psychostimulants, agents used for adhd and nootropics
## 460 n06b psychostimulants, agents used for adhd and nootropics
## 461 n06b psychostimulants, agents used for adhd and nootropics
## 462 n06a antidepressants
## 463 n07b drugs used in addictive disorders
## 464 n06a antidepressants
## 465 n05b anxiolytics
## 466 n06a antidepressants
## 467 v08a x-ray contrast media, iodinated
## 468 n06a antidepressants
## 469 n06b psychostimulants, agents used for adhd and nootropics
## 470 n05b anxiolytics
## 471 n06a antidepressants
## 472 n02a opioids
## 473 n06a antidepressants
## 474 n06a antidepressants
## 475 n03a antiepileptics
## 476 c08d selective calcium channel blockers with direct cardiac effects;c05a agents for treatment of hemorrhoids and anal fissures for topical use
## 477 n05c hypnotics and sedatives
## 478 j01m quinolone antibacterials [dg:dg01550];s01a antiinfectives
## 479 c10a lipid modifying agents, plain
## 480 n06a antidepressants
## 481 n06b psychostimulants, agents used for adhd and nootropics
## 482 g03c estrogens;l02a hormones and related agents
## 483 n05b anxiolytics
## 484 <NA>
## 485 <NA>
## 486 c10a lipid modifying agents, plain
## 487 n06a antidepressants
## 488 n06a antidepressants
## 489 n03a antiepileptics
## 490 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 491 n03a antiepileptics
## 492 <NA>
## 493 r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.;r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.
## 494 g03c estrogens;l02a hormones and related agents
## 495 g03c estrogens;l02a hormones and related agents
## 496 n02a opioids
## 497 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 498 n06a antidepressants
## 499 n06a antidepressants
## 500 c07a beta blocking agents
## 501 r06a antihistamines for systemic use
## 502 n06a antidepressants
## 503 n06a antidepressants
## 504 n06a antidepressants
## 505 l01e protein kinase inhibitors
## 506 a04a antiemetics and antinauseants;n05c hypnotics and sedatives;s01f mydriatics and cycloplegics
## 507 n05a antipsychotics
## 508 n06a antidepressants
## 509 n07b drugs used in addictive disorders
## 510 c02b antiadrenergic agents, ganglion-blocking
## 511 n06a antidepressants
## 512 n07b drugs used in addictive disorders;s01x other ophthalmologicals
## 513 p03a ectoparasiticides, incl. scabicides;n07b drugs used in addictive disorders
## 514 g04b urologicals;n04b dopaminergic agents
## 515 s01e antiglaucoma preparations and miotics;c07a beta blocking agents
## 516 m03b muscle relaxants, centrally acting agents
## 517 n03a antiepileptics
## 518 n05b anxiolytics
## 519 n05b anxiolytics
## 520 n03a antiepileptics
## 521 n05c hypnotics and sedatives
## 522 m03b muscle relaxants, centrally acting agents
## 523 n05b anxiolytics
## 524 n06a antidepressants
## 525 <NA>
## 526 <NA>
## 527 c08d selective calcium channel blockers with direct cardiac effects;c05a agents for treatment of hemorrhoids and anal fissures for topical use
## 528 n06a antidepressants
## 529 n06a antidepressants
## 530 n05a antipsychotics
## 531 n06a antidepressants
## 532 n03a antiepileptics
## 533 n06b psychostimulants, agents used for adhd and nootropics
## 534 n06a antidepressants
## 535 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 536 n06a antidepressants
## 537 n03a antiepileptics
## 538 n06a antidepressants
## 539 n06a antidepressants
## 540 n06a antidepressants
## 541 n06a antidepressants
## 542 <NA>
## 543 n02a opioids
## 544 n01b anesthetics, local
## 545 r02a throat preparations;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s02d other otologicals;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01b antiarrhythmics, class i and iii
## 546 n06a antidepressants
## 547 n06a antidepressants
## 548 <NA>
## 549 n06b psychostimulants, agents used for adhd and nootropics
## 550 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 551 n02a opioids
## 552 n06a antidepressants
## 553 n02a opioids
## 554 n02b other analgesics and antipyretics
## 555 <NA>
## 556 n03a antiepileptics
## 557 n02a opioids
## 558 n06a antidepressants
## 559 g03a hormonal contraceptives for systemic use;g03a hormonal contraceptives for systemic use
## 560 n06a antidepressants
## 561 n06a antidepressants
## 562 n05b anxiolytics
## 563 c02k other antihypertensives
## 564 n06a antidepressants
## 565 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 566 l02b hormone antagonists and related agents
## 567 l02b hormone antagonists and related agents
## 568 n06a antidepressants
## 569 n06a antidepressants
## 570 <NA>
## 571 n06a antidepressants
## 572 <NA>
## 573 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 574 g03c estrogens;l02a hormones and related agents
## 575 n06a antidepressants
## 576 n06a antidepressants
## 577 n07b drugs used in addictive disorders
## 578 n06a antidepressants
## 579 n05a antipsychotics
## 580 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 581 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 582 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 583 a01a stomatological preparations;j01a tetracyclines;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d06a antibiotics for topical use;s01a antiinfectives
## 584 n06a antidepressants
## 585 g03c estrogens;g03c estrogens
## 586 v03a all other therapeutic products;s01e antiglaucoma preparations and miotics
## 587 a04a antiemetics and antinauseants;n05c hypnotics and sedatives;s01f mydriatics and cycloplegics
## 588 <NA>
## 589 j01e sulfonamides and trimethoprim
## 590 j01e sulfonamides and trimethoprim
## 591 j01e sulfonamides and trimethoprim;j01e sulfonamides and trimethoprim
## 592 n06a antidepressants
## 593 n06a antidepressants
## 594 n06a antidepressants
## 595 n06a antidepressants
## 596 <NA>
## 597 n05a antipsychotics
## 598 c07a beta blocking agents
## 599 n06a antidepressants
## 600 n05b anxiolytics
## 601 r02a throat preparations;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s02d other otologicals;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01b antiarrhythmics, class i and iii
## 602 n06a antidepressants
## 603 n05b anxiolytics
## 604 v03a all other therapeutic products
## 605 n05b anxiolytics
## 606 c10a lipid modifying agents, plain
## 607 c10a lipid modifying agents, plain
## 608 n05a antipsychotics
## 609 n06a antidepressants
## 610 n06a antidepressants
## 611 n02a opioids
## 612 a04a antiemetics and antinauseants
## 613 n03a antiepileptics
## 614 n06a antidepressants
## 615 g03c estrogens;l02a hormones and related agents
## 616 n02a opioids
## 617 n06a antidepressants
## 618 n02a opioids
## 619 n05c hypnotics and sedatives
## 620 a07e intestinal antiinflammatory agents;a01a stomatological preparations;d07a corticosteroids, plain;d07x corticosteroids, other combinations;s02b corticosteroids;s01c antiinflammatory agents and antiinfectives in combination;s01b antiinflammatory agents;h02a corticosteroids for systemic use, plain;c05a agents for treatment of hemorrhoids and anal fissures for topical use;a07e intestinal antiinflammatory agents;a01a stomatological preparations;d07a corticosteroids, plain;d07x corticosteroids, other combinations;s02b corticosteroids;s01c antiinflammatory agents and antiinfectives in combination;s01b antiinflammatory agents;h02a corticosteroids for systemic use, plain;c05a agents for treatment of hemorrhoids and anal fissures for topical use
## 621 n06a antidepressants
## 622 n06a antidepressants
## 623 n06a antidepressants
## 624 n05b anxiolytics
## 625 c03d aldosterone antagonists and other potassium-sparing agents
## 626 g03c estrogens;g03c estrogens
## 627 <NA>
## 628 n06a antidepressants
## 629 n06a antidepressants
## 630 c07a beta blocking agents
## 631 a01a stomatological preparations;j01a tetracyclines;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d06a antibiotics for topical use;s01a antiinfectives
## 632 n05a antipsychotics
## 633 g03c estrogens;l02a hormones and related agents
## 634 n05b anxiolytics
## 635 n03a antiepileptics
## 636 n05b anxiolytics
## 637 n06a antidepressants
## 638 n05a antipsychotics
## 639 n05b anxiolytics
## 640 c10a lipid modifying agents, plain
## 641 n06a antidepressants
## 642 n02b other analgesics and antipyretics
## 643 j01e sulfonamides and trimethoprim
## 644 n06a antidepressants
## 645 n06a antidepressants
## 646 n03a antiepileptics
## 647 n03a antiepileptics
## 648 n03a antiepileptics
## 649 n03a antiepileptics
## 650 n03a antiepileptics;n03a antiepileptics
## 651 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 652 n06a antidepressants
## 653 n05b anxiolytics
## 654 n06a antidepressants
## 655 n03a antiepileptics
## 656 n02a opioids
## 657 n02a opioids
## 658 n06a antidepressants
## 659 n06a antidepressants
## 660 n06a antidepressants
## 661 n06a antidepressants
## 662 d04a antipruritics, incl. antihistamines, anesthetics, etc.;n06a antidepressants
## 663 n06a antidepressants
## 664 n06a antidepressants
## 665 n04b dopaminergic agents
## 666 n06a antidepressants
## 667 n06a antidepressants
## 668 n06a antidepressants
## 669 n06a antidepressants
## 670 n06a antidepressants
## 671 n06a antidepressants
## 672 n06b psychostimulants, agents used for adhd and nootropics
## 673 n06a antidepressants
## 674 n06a antidepressants
## 675 n06a antidepressants
## 676 n06a antidepressants
## 677 l02b hormone antagonists and related agents
## 678 n06a antidepressants
## 679 n04b dopaminergic agents
## 680 n06a antidepressants
## 681 a06a drugs for constipation;v03a all other therapeutic products
## 682 g03c estrogens;l02a hormones and related agents
## 683 g03c estrogens;l02a hormones and related agents
## 684 n06a antidepressants
## 685 g04c drugs used in benign prostatic hypertrophy;d11a other dermatological preparations
## 686 <NA>
## 687 n06a antidepressants
## 688 n06a antidepressants
## 689 n06b psychostimulants, agents used for adhd and nootropics
## 690 n02a opioids
## 691 n05b anxiolytics
## 692 n05b anxiolytics
## 693 n05b anxiolytics
## 694 n05c hypnotics and sedatives
## 695 n06b psychostimulants, agents used for adhd and nootropics
## 696 r02a throat preparations;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s02d other otologicals;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01b antiarrhythmics, class i and iii
## 697 n06a antidepressants
## 698 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 699 n03a antiepileptics
## 700 n06a antidepressants
## 701 n02a opioids
## 702 n06a antidepressants
## 703 n05a antipsychotics
## 704 n06a antidepressants
## 705 n06a antidepressants
## 706 g03c estrogens;l02a hormones and related agents
## 707 <NA>
## 708 a04a antiemetics and antinauseants;n05c hypnotics and sedatives;s01f mydriatics and cycloplegics
## 709 n05c hypnotics and sedatives
## 710 n06a antidepressants
## 711 n06a antidepressants
## 712 n06a antidepressants
## 713 n06a antidepressants
## 714 <NA>
## 715 <NA>
## 716 l04a immunosuppressants;s01x other ophthalmologicals
## 717 g03c estrogens;l02a hormones and related agents
## 718 a03b belladonna and derivatives, plain;s01f mydriatics and cycloplegics
## 719 a04a antiemetics and antinauseants;n05c hypnotics and sedatives;s01f mydriatics and cycloplegics
## 720 g03c estrogens;l02a hormones and related agents
## 721 g03a hormonal contraceptives for systemic use;g03d progestogens
## 722 n06a antidepressants
## 723 n02a opioids
## 724 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 725 n01a anesthetics, general
## 726 <NA>
## 727 n06a antidepressants
## 728 n06a antidepressants
## 729 n06a antidepressants
## 730 a06a drugs for constipation;v03a all other therapeutic products
## 731 <NA>
## 732 v03a all other therapeutic products;v04c other diagnostic agents
## 733 n01a anesthetics, general
## 734 c01e other cardiac preparations
## 735 c03d aldosterone antagonists and other potassium-sparing agents
## 736 m01a antiinflammatory and antirheumatic products, non-steroids
## 737 n02a opioids
## 738 n06a antidepressants
## 739 n06a antidepressants
## 740 g03c estrogens;l02a hormones and related agents
## 741 n07b drugs used in addictive disorders
## 742 j01f macrolides, lincosamides and streptogramins;d10a anti-acne preparations for topical use;s01a antiinfectives
## 743 h02c antiadrenal preparations;j02a antimycotics for systemic use;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d01a antifungals for topical use;h02c antiadrenal preparations
## 744 n05b anxiolytics
## 745 <NA>
## 746 n05c hypnotics and sedatives
## 747 c09c angiotensin ii receptor blockers (arbs), plain
## 748 c07a beta blocking agents
## 749 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 750 n03a antiepileptics
## 751 n05b anxiolytics
## 752 n06a antidepressants
## 753 n06a antidepressants
## 754 n05b anxiolytics
## 755 n06a antidepressants
## 756 a08a antiobesity preparations, excl. diet products
## 757 n05b anxiolytics
## 758 c02d arteriolar smooth muscle, agents acting on
## 759 c08d selective calcium channel blockers with direct cardiac effects
## 760 n06a antidepressants
## 761 <NA>
## 762 <NA>
## 763 c07a beta blocking agents
## 764 c07a beta blocking agents
## 765 c07a beta blocking agents
## 766 c07a beta blocking agents
## 767 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 768 <NA>
## 769 g03c estrogens;g03c estrogens
## 770 g03e androgens and female sex hormones in combination;g03b androgens;g03e androgens and female sex hormones in combination;g03b androgens
## 771 d01a antifungals for topical use;s01b antiinflammatory agents
## 772 n02a opioids
## 773 m03b muscle relaxants, centrally acting agents
## 774 n05c hypnotics and sedatives
## 775 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 776 n02a opioids
## 777 a06a drugs for constipation;v03a all other therapeutic products
## 778 c07a beta blocking agents
## 779 <NA>
## 780 <NA>
## 781 n06a antidepressants
## 782 n06a antidepressants
## 783 n05b anxiolytics
## 784 n06a antidepressants
## 785 n06a antidepressants
## 786 n05b anxiolytics
## 787 n03a antiepileptics
## 788 n06a antidepressants
## 789 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 790 g03c estrogens;l02a hormones and related agents
## 791 <NA>
## 792 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 793 b01a antithrombotic agents
## 794 n05a antipsychotics
## 795 c02k other antihypertensives
## 796 a04a antiemetics and antinauseants
## 797 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 798 n05b anxiolytics
## 799 n06a antidepressants
## 800 n02a opioids
## 801 n06a antidepressants
## 802 n05b anxiolytics
## 803 g03c estrogens;l02a hormones and related agents
## 804 n05b anxiolytics
## 805 n03a antiepileptics
## 806 n05b anxiolytics
## 807 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 808 n06a antidepressants
## 809 n06a antidepressants
## 810 n05c hypnotics and sedatives
## 811 n07b drugs used in addictive disorders
## 812 n05b anxiolytics
## 813 <NA>
## 814 n05c hypnotics and sedatives
## 815 n06a antidepressants
## 816 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 817 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 818 n03a antiepileptics
## 819 c10a lipid modifying agents, plain
## 820 j01e sulfonamides and trimethoprim
## 821 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 822 j01f macrolides, lincosamides and streptogramins
## 823 n06a antidepressants
## 824 r02a throat preparations;d02b protectives against uv-radiation;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;c05a agents for treatment of hemorrhoids and anal fissures for topical use
## 825 <NA>
## 826 n06a antidepressants
## 827 n05b anxiolytics
## 828 n05b anxiolytics
## 829 g03c estrogens
## 830 g03c estrogens;g03c estrogens
## 831 n05c hypnotics and sedatives
## 832 g03c estrogens;g03c estrogens
## 833 n05b anxiolytics
## 834 n05c hypnotics and sedatives
## 835 n06a antidepressants
## 836 c07a beta blocking agents
## 837 n06b psychostimulants, agents used for adhd and nootropics
## 838 r02a throat preparations;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s02d other otologicals;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01b antiarrhythmics, class i and iii
## 839 a07e intestinal antiinflammatory agents;h02a corticosteroids for systemic use, plain
## 840 n06a antidepressants
## 841 a10b blood glucose lowering drugs, excl. insulins
## 842 n01a anesthetics, general
## 843 n01a anesthetics, general
## 844 n05c hypnotics and sedatives
## 845 n01a anesthetics, general
## 846 n01a anesthetics, general
## 847 n05c hypnotics and sedatives
## 848 <NA>
## 849 n06a antidepressants
## 850 n05c hypnotics and sedatives
## 851 n03a antiepileptics
## 852 a04a antiemetics and antinauseants
## 853 <NA>
## 854 <NA>
## 855 n06a antidepressants
## 856 <NA>
## 857 <NA>
## 858 n06a antidepressants
## 859 n06a antidepressants
## 860 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 861 n06a antidepressants
## 862 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 863 c07a beta blocking agents
## 864 n06a antidepressants
## 865 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 866 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 867 b01a antithrombotic agents;a01a stomatological preparations;n02b other analgesics and antipyretics
## 868 j01c beta-lactam antibacterials, penicillins
## 869 c03a low-ceiling diuretics, thiazides
## 870 c03c high-ceiling diuretics
## 871 c07a beta blocking agents
## 872 n05b anxiolytics
## 873 c01a cardiac glycosides
## 874 c08c selective calcium channel blockers with mainly vascular effects
## 875 n06a antidepressants
## 876 n05b anxiolytics
## 877 n05a antipsychotics
## 878 n06a antidepressants
## 879 n05a antipsychotics
## 880 n06a antidepressants
## 881 n05b anxiolytics
## 882 n06a antidepressants
## 883 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 884 r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.;r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.
## 885 n05b anxiolytics
## 886 l01c plant alkaloids and other natural products
## 887 l01x other antineoplastic agents
## 888 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 889 n06a antidepressants
## 890 n06a antidepressants
## 891 <NA>
## 892 n06a antidepressants
## 893 a10b blood glucose lowering drugs, excl. insulins
## 894 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 895 n06a antidepressants
## 896 n03a antiepileptics
## 897 n05b anxiolytics
## 898 c07a beta blocking agents
## 899 n05b anxiolytics
## 900 n06a antidepressants
## 901 a02b drugs for peptic ulcer and gastro-oesophageal reflux disease (gord)
## 902 g03c estrogens;g03c estrogens
## 903 a06a drugs for constipation;v03a all other therapeutic products
## 904 n05b anxiolytics
## 905 c08d selective calcium channel blockers with direct cardiac effects
## 906 j01m quinolone antibacterials [dg:dg01550];s02a antiinfectives;s03a antiinfectives;s01a antiinfectives
## 907 <NA>
## 908 j01m quinolone antibacterials [dg:dg01550];s01a antiinfectives
## 909 <NA>
## 910 a01a stomatological preparations;j01a tetracyclines;d06a antibiotics for topical use;s01a antiinfectives
## 911 a01a stomatological preparations;j01a tetracyclines
## 912 g04b urologicals;n04b dopaminergic agents
## 913 n06a antidepressants
## 914 <NA>
## 915 n02a opioids
## 916 n06a antidepressants
## 917 n02c antimigraine preparations
## 918 g03c estrogens;l02a hormones and related agents
## 919 r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.;r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.
## 920 n03a antiepileptics;n03a antiepileptics
## 921 n03a antiepileptics
## 922 c10a lipid modifying agents, plain
## 923 c07a beta blocking agents
## 924 c07a beta blocking agents
## 925 a02b drugs for peptic ulcer and gastro-oesophageal reflux disease (gord)
## 926 n06a antidepressants
## 927 g03c estrogens;g03c estrogens
## 928 <NA>
## 929 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 930 n03a antiepileptics
## 931 n05b anxiolytics
## 932 n06a antidepressants
## 933 n06a antidepressants
## 934 n06a antidepressants
## 935 n05a antipsychotics
## 936 n05a antipsychotics
## 937 c07a beta blocking agents
## 938 n06a antidepressants
## 939 g03c estrogens;l02a hormones and related agents
## 940 n05b anxiolytics
## 941 n06b psychostimulants, agents used for adhd and nootropics
## 942 v03a all other therapeutic products;r05c expectorants, excl. combinations with cough suppressants;s01x other ophthalmologicals
## 943 n07b drugs used in addictive disorders
## 944 c02b antiadrenergic agents, ganglion-blocking
## 945 n03a antiepileptics;n03a antiepileptics
## 946 n06a antidepressants
## 947 n06a antidepressants
## 948 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 949 n07a parasympathomimetics;s01e antiglaucoma preparations and miotics
## 950 <NA>
## 951 n06d anti-dementia drugs
## 952 n06a antidepressants
## 953 n06a antidepressants
## 954 n06a antidepressants
## 955 n06a antidepressants
## 956 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 957 g03c estrogens;g03c estrogens
## 958 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 959 g03c estrogens;g03c estrogens
## 960 g03c estrogens;l02a hormones and related agents
## 961 g03b androgens
## 962 <NA>
## 963 a07e intestinal antiinflammatory agents;a01a stomatological preparations;d07a corticosteroids, plain;d07x corticosteroids, other combinations;s02b corticosteroids;s01c antiinflammatory agents and antiinfectives in combination;s01b antiinflammatory agents;h02a corticosteroids for systemic use, plain;c05a agents for treatment of hemorrhoids and anal fissures for topical use;a07e intestinal antiinflammatory agents;a01a stomatological preparations;d07a corticosteroids, plain;d07x corticosteroids, other combinations;s02b corticosteroids;s01c antiinflammatory agents and antiinfectives in combination;s01b antiinflammatory agents;h02a corticosteroids for systemic use, plain;c05a agents for treatment of hemorrhoids and anal fissures for topical use
## 964 g03x other sex hormones and modulators of the genital system
## 965 <NA>
## 966 l02b hormone antagonists and related agents
## 967 l02b hormone antagonists and related agents
## 968 h03b antithyroid preparations
## 969 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 970 a14a anabolic steroids;g03b androgens
## 971 <NA>
## 972 c10a lipid modifying agents, plain
## 973 n06a antidepressants
## 974 n06a antidepressants
## 975 n05b anxiolytics
## 976 n03a antiepileptics
## 977 c07a beta blocking agents
## 978 <NA>
## 979 <NA>
## 980 <NA>
## 981 <NA>
## 982 g03c estrogens;g03c estrogens
## 983 n06a antidepressants
## 984 n06a antidepressants
## 985 n06a antidepressants
## 986 c02a antiadrenergic agents, centrally acting
## 987 n06a antidepressants
## 988 n05b anxiolytics
## 989 n06a antidepressants
## 990 g03a hormonal contraceptives for systemic use
## 991 n05b anxiolytics
## 992 <NA>
## 993 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 994 n06a antidepressants
## 995 n06a antidepressants
## 996 n06b psychostimulants, agents used for adhd and nootropics
## 997 n05b anxiolytics
## 998 n06a antidepressants
## 999 n06a antidepressants
## 1000 <NA>
## 1001 <NA>
## 1002 n06a antidepressants
## 1003 <NA>
## 1004 r03c adrenergics for systemic use;r03a adrenergics, inhalants;c01c cardiac stimulants excl. cardiac glycosides
## 1005 c07a beta blocking agents
## 1006 r01b nasal decongestants for systemic use;r01a decongestants and other nasal preparations for topical use;s01g decongestants and antiallergics;s01f mydriatics and cycloplegics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01c cardiac stimulants excl. cardiac glycosides
## 1007 g04b urologicals;n04b dopaminergic agents
## 1008 n05a antipsychotics
## 1009 a03b belladonna and derivatives, plain;s01f mydriatics and cycloplegics
## 1010 c02b antiadrenergic agents, ganglion-blocking
## 1011 c07a beta blocking agents
## 1012 c07a beta blocking agents
## 1013 c07a beta blocking agents
## 1014 s01e antiglaucoma preparations and miotics
## 1015 c07a beta blocking agents
## 1016 n05b anxiolytics
## 1017 <NA>
## 1018 n02a opioids
## 1019 <NA>
## 1020 <NA>
## 1021 n05b anxiolytics
## 1022 c01c cardiac stimulants excl. cardiac glycosides
## 1023 n05c hypnotics and sedatives
## 1024 n06b psychostimulants, agents used for adhd and nootropics
## 1025 n02a opioids
## 1026 n06a antidepressants
## 1027 c07a beta blocking agents
## 1028 c10a lipid modifying agents, plain
## 1029 <NA>
## 1030 n02a opioids
## 1031 n07b drugs used in addictive disorders
## 1032 <NA>
## 1033 <NA>
## 1034 <NA>
## 1035 <NA>
## 1036 <NA>
## 1037 s01e antiglaucoma preparations and miotics
## 1038 n05b anxiolytics
## 1039 n06a antidepressants
## 1040 c10a lipid modifying agents, plain
## 1041 n05a antipsychotics
## 1042 g03c estrogens;g03c estrogens
## 1043 n06b psychostimulants, agents used for adhd and nootropics
## 1044 <NA>
## 1045 <NA>
## 1046 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 1047 <NA>
## 1048 b01a antithrombotic agents;a01a stomatological preparations;n02b other analgesics and antipyretics
## 1049 n03a antiepileptics
## 1050 n05b anxiolytics
## 1051 n03a antiepileptics
## 1052 n02b other analgesics and antipyretics
## 1053 n03a antiepileptics
## 1054 n03a antiepileptics
## 1055 n03a antiepileptics
## 1056 n03a antiepileptics;n03a antiepileptics
## 1057 n03a antiepileptics
## 1058 n03a antiepileptics
## 1059 n03a antiepileptics
## 1060 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 1061 n03a antiepileptics
## 1062 <NA>
## 1063 <NA>
## 1064 n05a antipsychotics
## 1065 n06b psychostimulants, agents used for adhd and nootropics;n06b psychostimulants, agents used for adhd and nootropics
## 1066 <NA>
## 1067 j01e sulfonamides and trimethoprim;j01e sulfonamides and trimethoprim
## 1068 n01a anesthetics, general
## 1069 n01a anesthetics, general
## 1070 n01a anesthetics, general
## 1071 n01a anesthetics, general
## 1072 n01a anesthetics, general
## 1073 n01a anesthetics, general
## 1074 n05c hypnotics and sedatives
## 1075 n03a antiepileptics
## 1076 n05b anxiolytics
## 1077 <NA>
## 1078 n03a antiepileptics
## 1079 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 1080 <NA>
## 1081 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 1082 b01a antithrombotic agents;a01a stomatological preparations;n02b other analgesics and antipyretics
## 1083 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 1084 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 1085 n03a antiepileptics
## 1086 s01e antiglaucoma preparations and miotics
## 1087 <NA>
## 1088 s01e antiglaucoma preparations and miotics
## 1089 j01f macrolides, lincosamides and streptogramins;d10a anti-acne preparations for topical use;s01a antiinfectives
## 1090 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 1091 c03c high-ceiling diuretics
## 1092 j01e sulfonamides and trimethoprim
## 1093 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1094 g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;s02a antiinfectives
## 1095 n05a antipsychotics
## 1096 <NA>
## 1097 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1098 n06a antidepressants
## 1099 n05b anxiolytics
## 1100 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids
## 1101 n02a opioids
## 1102 a06a drugs for constipation;v03a all other therapeutic products
## 1103 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1104 n05b anxiolytics
## 1105 n06a antidepressants
## 1106 j01f macrolides, lincosamides and streptogramins
## 1107 <NA>
## 1108 j01e sulfonamides and trimethoprim;j01e sulfonamides and trimethoprim
## 1109 g03c estrogens;l02a hormones and related agents
## 1110 g03c estrogens;l02a hormones and related agents
## 1111 g03d progestogens
## 1112 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 1113 n03a antiepileptics
## 1114 n03a antiepileptics
## 1115 n03a antiepileptics
## 1116 l02b hormone antagonists and related agents
## 1117 n05b anxiolytics
## 1118 n05a antipsychotics
## 1119 c08d selective calcium channel blockers with direct cardiac effects;c05a agents for treatment of hemorrhoids and anal fissures for topical use
## 1120 <NA>
## 1121 c02k other antihypertensives
## 1122 n05b anxiolytics
## 1123 m02a topical products for joint and muscular pain;n01b anesthetics, local
## 1124 a06a drugs for constipation;v03a all other therapeutic products
## 1125 n01a anesthetics, general
## 1126 c01e other cardiac preparations
## 1127 c03d aldosterone antagonists and other potassium-sparing agents
## 1128 n02a opioids
## 1129 g03c estrogens;l02a hormones and related agents
## 1130 n02a opioids
## 1131 g03c estrogens;g03c estrogens
## 1132 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 1133 n06a antidepressants
## 1134 <NA>
## 1135 n06a antidepressants
## 1136 v03a all other therapeutic products;r05c expectorants, excl. combinations with cough suppressants;s01x other ophthalmologicals
## 1137 n05a antipsychotics
## 1138 n05a antipsychotics
## 1139 n06a antidepressants
## 1140 n05a antipsychotics
## 1141 n07b drugs used in addictive disorders
## 1142 n06a antidepressants
## 1143 n06a antidepressants
## 1144 n02c antimigraine preparations
## 1145 a04a antiemetics and antinauseants
## 1146 r06a antihistamines for systemic use
## 1147 n05b anxiolytics
## 1148 n06a antidepressants
## 1149 g03c estrogens;l02a hormones and related agents
## 1150 c02a antiadrenergic agents, centrally acting
## 1151 <NA>
## 1152 <NA>
## 1153 n05b anxiolytics
## 1154 <NA>
## 1155 <NA>
## 1156 <NA>
## 1157 n05a antipsychotics
## 1158 <NA>
## 1159 n06a antidepressants
## 1160 g03c estrogens;l02a hormones and related agents
## 1161 n06a antidepressants
## 1162 n02a opioids
## 1163 n01a anesthetics, general
## 1164 n06b psychostimulants, agents used for adhd and nootropics
## 1165 r02a throat preparations;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s02d other otologicals;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01b antiarrhythmics, class i and iii
## 1166 n02a opioids
## 1167 b01a antithrombotic agents;a01a stomatological preparations;n02b other analgesics and antipyretics
## 1168 n01a anesthetics, general
## 1169 n06a antidepressants
## 1170 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 1171 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1172 d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use
## 1173 r02a throat preparations;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s02d other otologicals;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01b antiarrhythmics, class i and iii
## 1174 n01b anesthetics, local
## 1175 n06a antidepressants
## 1176 n05b anxiolytics
## 1177 <NA>
## 1178 g03c estrogens;l02a hormones and related agents
## 1179 l01e protein kinase inhibitors
## 1180 <NA>
## 1181 g03e androgens and female sex hormones in combination;g03b androgens;g03e androgens and female sex hormones in combination;g03b androgens
## 1182 <NA>
## 1183 <NA>
## 1184 <NA>
## 1185 <NA>
## 1186 g03c estrogens;l02a hormones and related agents
## 1187 n02a opioids
## 1188 <NA>
## 1189 <NA>
## 1190 n06a antidepressants
## 1191 <NA>
## 1192 a06a drugs for constipation
## 1193 <NA>
## 1194 n01a anesthetics, general
## 1195 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 1196 n05b anxiolytics
## 1197 n05a antipsychotics
## 1198 n02a opioids
## 1199 a04a antiemetics and antinauseants
## 1200 <NA>
## 1201 n07b drugs used in addictive disorders
## 1202 g03d progestogens
## 1203 g03a hormonal contraceptives for systemic use;g03a hormonal contraceptives for systemic use
## 1204 n06b psychostimulants, agents used for adhd and nootropics
## 1205 n05b anxiolytics
## 1206 n06a antidepressants
## 1207 n06b psychostimulants, agents used for adhd and nootropics
## 1208 n06a antidepressants
## 1209 v04c other diagnostic agents
## 1210 n06a antidepressants
## 1211 n05b anxiolytics
## 1212 n06a antidepressants
## 1213 n06a antidepressants
## 1214 n06a antidepressants
## 1215 n06a antidepressants
## 1216 a04a antiemetics and antinauseants
## 1217 g03c estrogens;l02a hormones and related agents
## 1218 g03c estrogens;g03c estrogens
## 1219 n06a antidepressants
## 1220 c10a lipid modifying agents, plain
## 1221 a10b blood glucose lowering drugs, excl. insulins
## 1222 n05b anxiolytics
## 1223 a02b drugs for peptic ulcer and gastro-oesophageal reflux disease (gord)
## 1224 <NA>
## 1225 n06b psychostimulants, agents used for adhd and nootropics
## 1226 <NA>
## 1227 n06b psychostimulants, agents used for adhd and nootropics
## 1228 g03c estrogens;l02a hormones and related agents
## 1229 g03c estrogens;g03c estrogens
## 1230 g03c estrogens;l02a hormones and related agents
## 1231 j01d other beta-lactam antibacterials
## 1232 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 1233 n03a antiepileptics
## 1234 n06a antidepressants
## 1235 r03c adrenergics for systemic use;r03a adrenergics, inhalants;c01c cardiac stimulants excl. cardiac glycosides
## 1236 g04b urologicals;n04b dopaminergic agents
## 1237 n05b anxiolytics
## 1238 n06a antidepressants
## 1239 <NA>
## 1240 <NA>
## 1241 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids
## 1242 n06a antidepressants
## 1243 v03a all other therapeutic products;s01e antiglaucoma preparations and miotics
## 1244 n07b drugs used in addictive disorders
## 1245 n05b anxiolytics
## 1246 v03a all other therapeutic products
## 1247 n05a antipsychotics
## 1248 n06a antidepressants
## 1249 n05b anxiolytics
## 1250 <NA>
## 1251 n06b psychostimulants, agents used for adhd and nootropics
## 1252 <NA>
## 1253 n07b drugs used in addictive disorders
## 1254 <NA>
## 1255 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1256 n05a antipsychotics
## 1257 n04b dopaminergic agents
## 1258 n05a antipsychotics
## 1259 n05a antipsychotics
## 1260 g03c estrogens;l02a hormones and related agents
## 1261 n06a antidepressants
## 1262 n06a antidepressants
## 1263 n06a antidepressants
## 1264 n05b anxiolytics
## 1265 d05a antipsoriatics for topical use;d05b antipsoriatics for systemic use
## 1266 n06a antidepressants
## 1267 n06b psychostimulants, agents used for adhd and nootropics
## 1268 n06a antidepressants
## 1269 n06a antidepressants
## 1270 j01m quinolone antibacterials [dg:dg01550];s02a antiinfectives;s03a antiinfectives;s01a antiinfectives
## 1271 n05a antipsychotics
## 1272 r03c adrenergics for systemic use;r03a adrenergics, inhalants;c01c cardiac stimulants excl. cardiac glycosides
## 1273 <NA>
## 1274 <NA>
## 1275 n05b anxiolytics
## 1276 g03c estrogens;g03c estrogens
## 1277 g03c estrogens;g03c estrogens
## 1278 c10a lipid modifying agents, plain
## 1279 n03a antiepileptics
## 1280 g03c estrogens;g03c estrogens
## 1281 g03d progestogens
## 1282 <NA>
## 1283 n06a antidepressants
## 1284 n05b anxiolytics
## 1285 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1286 n06a antidepressants
## 1287 n06a antidepressants
## 1288 n06a antidepressants
## 1289 n06a antidepressants
## 1290 g03a hormonal contraceptives for systemic use;g03a hormonal contraceptives for systemic use
## 1291 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 1292 n05b anxiolytics
## 1293 <NA>
## 1294 <NA>
## 1295 a03f propulsives
## 1296 n03a antiepileptics
## 1297 a01a stomatological preparations;p01a agents against amoebiasis and other protozoal diseases;j01x other antibacterials;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d06b chemotherapeutics for topical use
## 1298 <NA>
## 1299 j01m quinolone antibacterials [dg:dg01550]
## 1300 a01a stomatological preparations;j01a tetracyclines;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d06a antibiotics for topical use;s01a antiinfectives
## 1301 a07a intestinal antiinfectives;j01g aminoglycoside antibacterials [dg:dg01447]
## 1302 j01e sulfonamides and trimethoprim
## 1303 a01a stomatological preparations;j01a tetracyclines;d06a antibiotics for topical use;s02a antiinfectives;s03a antiinfectives;s01a antiinfectives
## 1304 <NA>
## 1305 <NA>
## 1306 <NA>
## 1307 n05b anxiolytics
## 1308 c07a beta blocking agents
## 1309 g04b urologicals
## 1310 c07a beta blocking agents
## 1311 r06a antihistamines for systemic use
## 1312 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 1313 m01a antiinflammatory and antirheumatic products, non-steroids
## 1314 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals
## 1315 m01a antiinflammatory and antirheumatic products, non-steroids
## 1316 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids
## 1317 p03b insecticides and repellents
## 1318 <NA>
## 1319 n03a antiepileptics
## 1320 j01f macrolides, lincosamides and streptogramins
## 1321 <NA>
## 1322 g04b urologicals;n04b dopaminergic agents
## 1323 g02c other gynecologicals;n04b dopaminergic agents
## 1324 n04b dopaminergic agents
## 1325 n05a antipsychotics
## 1326 <NA>
## 1327 n05a antipsychotics
## 1328 j01m quinolone antibacterials [dg:dg01550];s01a antiinfectives
## 1329 a07a intestinal antiinfectives;j01g aminoglycoside antibacterials [dg:dg01447]
## 1330 g03c estrogens
## 1331 <NA>
## 1332 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1333 g03c estrogens;l02a hormones and related agents
## 1334 n06a antidepressants
## 1335 n06a antidepressants
## 1336 g03c estrogens;l02a hormones and related agents
## 1337 n06b psychostimulants, agents used for adhd and nootropics
## 1338 n06b psychostimulants, agents used for adhd and nootropics
## 1339 n05a antipsychotics
## 1340 n05b anxiolytics
## 1341 v03a all other therapeutic products
## 1342 n03a antiepileptics
## 1343 n03a antiepileptics
## 1344 n03a antiepileptics
## 1345 n03a antiepileptics
## 1346 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 1347 b01a antithrombotic agents;a01a stomatological preparations;n02b other analgesics and antipyretics
## 1348 n02a opioids
## 1349 r02a throat preparations;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s02d other otologicals;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01b antiarrhythmics, class i and iii
## 1350 <NA>
## 1351 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 1352 n05b anxiolytics
## 1353 <NA>
## 1354 g04b urologicals;n04b dopaminergic agents
## 1355 a07a intestinal antiinfectives
## 1356 <NA>
## 1357 j01f macrolides, lincosamides and streptogramins;d10a anti-acne preparations for topical use;s01a antiinfectives
## 1358 j01f macrolides, lincosamides and streptogramins
## 1359 g03c estrogens;l02a hormones and related agents
## 1360 r06a antihistamines for systemic use;d04a antipruritics, incl. antihistamines, anesthetics, etc.
## 1361 g03a hormonal contraceptives for systemic use;g03a hormonal contraceptives for systemic use
## 1362 <NA>
## 1363 n06a antidepressants
## 1364 l01x other antineoplastic agents
## 1365 n06a antidepressants
## 1366 n06a antidepressants
## 1367 n05b anxiolytics
## 1368 g04b urologicals
## 1369 n07b drugs used in addictive disorders
## 1370 c10a lipid modifying agents, plain
## 1371 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 1372 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals
## 1373 n03a antiepileptics
## 1374 c10a lipid modifying agents, plain
## 1375 j01e sulfonamides and trimethoprim
## 1376 a01a stomatological preparations;j01a tetracyclines;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d06a antibiotics for topical use;s01a antiinfectives
## 1377 <NA>
## 1378 j01e sulfonamides and trimethoprim
## 1379 j01e sulfonamides and trimethoprim
## 1380 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1381 n07b drugs used in addictive disorders
## 1382 n06a antidepressants
## 1383 n03a antiepileptics
## 1384 n05a antipsychotics
## 1385 n03a antiepileptics
## 1386 g04b urologicals
## 1387 n06a antidepressants
## 1388 <NA>
## 1389 n05a antipsychotics
## 1390 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1391 a08a antiobesity preparations, excl. diet products
## 1392 a10b blood glucose lowering drugs, excl. insulins
## 1393 l01x other antineoplastic agents
## 1394 c10a lipid modifying agents, plain
## 1395 c10a lipid modifying agents, plain
## 1396 c02a antiadrenergic agents, centrally acting
## 1397 n05a antipsychotics
## 1398 d07a corticosteroids, plain;d10a anti-acne preparations for topical use;h02a corticosteroids for systemic use, plain
## 1399 n06a antidepressants
## 1400 <NA>
## 1401 <NA>
## 1402 b01a antithrombotic agents
## 1403 g03c estrogens;g03c estrogens
## 1404 l01b antimetabolites
## 1405 n02a opioids
## 1406 g03b androgens
## 1407 n05a antipsychotics
## 1408 n05a antipsychotics
## 1409 <NA>
## 1410 n06b psychostimulants, agents used for adhd and nootropics
## 1411 n06a antidepressants
## 1412 a10b blood glucose lowering drugs, excl. insulins
## 1413 <NA>
## 1414 a07e intestinal antiinflammatory agents;a01a stomatological preparations;d07a corticosteroids, plain;d07x corticosteroids, other combinations;s02b corticosteroids;s01c antiinflammatory agents and antiinfectives in combination;s01b antiinflammatory agents;h02a corticosteroids for systemic use, plain;c05a agents for treatment of hemorrhoids and anal fissures for topical use;a07e intestinal antiinflammatory agents;a01a stomatological preparations;d07a corticosteroids, plain;d07x corticosteroids, other combinations;s02b corticosteroids;s01c antiinflammatory agents and antiinfectives in combination;s01b antiinflammatory agents;h02a corticosteroids for systemic use, plain;c05a agents for treatment of hemorrhoids and anal fissures for topical use
## 1415 j01f macrolides, lincosamides and streptogramins
## 1416 l01d cytotoxic antibiotics and related substances
## 1417 h02c antiadrenal preparations;j02a antimycotics for systemic use;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d01a antifungals for topical use;h02c antiadrenal preparations
## 1418 c02d arteriolar smooth muscle, agents acting on
## 1419 r03d other systemic drugs for obstructive airway diseases
## 1420 v03a all other therapeutic products;v04c other diagnostic agents
## 1421 n04b dopaminergic agents
## 1422 n02c antimigraine preparations;s01e antiglaucoma preparations and miotics;c02a antiadrenergic agents, centrally acting
## 1423 n06b psychostimulants, agents used for adhd and nootropics
## 1424 b05c irrigating solutions;v06d other nutrients;v04c other diagnostic agents
## 1425 l01a alkylating agents
## 1426 g03d progestogens
## 1427 v03a all other therapeutic products
## 1428 n06a antidepressants
## 1429 n06a antidepressants
## 1430 s01e antiglaucoma preparations and miotics;c07a beta blocking agents
## 1431 g03c estrogens;g03c estrogens
## 1432 <NA>
## 1433 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 1434 n03a antiepileptics
## 1435 n06a antidepressants
## 1436 n05b anxiolytics
## 1437 l02b hormone antagonists and related agents
## 1438 n06a antidepressants
## 1439 a16a other alimentary tract and metabolism products
## 1440 <NA>
## 1441 <NA>
## 1442 <NA>
## 1443 l01a alkylating agents
## 1444 n06a antidepressants
## 1445 c02a antiadrenergic agents, centrally acting
## 1446 n06a antidepressants
## 1447 n06a antidepressants
## 1448 g04b urologicals;n04b dopaminergic agents
## 1449 n06a antidepressants
## 1450 n07b drugs used in addictive disorders
## 1451 n05b anxiolytics
## 1452 n05b anxiolytics
## 1453 n05b anxiolytics
## 1454 c02a antiadrenergic agents, centrally acting
## 1455 n05c hypnotics and sedatives
## 1456 n06a antidepressants
## 1457 <NA>
## 1458 n06a antidepressants
## 1459 n06a antidepressants
## 1460 <NA>
## 1461 <NA>
## 1462 n06a antidepressants
## 1463 n06a antidepressants
## 1464 n06a antidepressants
## 1465 n05b anxiolytics
## 1466 l02b hormone antagonists and related agents
## 1467 r06a antihistamines for systemic use;s01g decongestants and antiallergics
## 1468 n06a antidepressants
## 1469 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1470 c07a beta blocking agents
## 1471 c07a beta blocking agents
## 1472 c10a lipid modifying agents, plain
## 1473 c10a lipid modifying agents, plain
## 1474 d01a antifungals for topical use;s01b antiinflammatory agents
## 1475 d01a antifungals for topical use;s01b antiinflammatory agents
## 1476 a07e intestinal antiinflammatory agents;a01a stomatological preparations;r01a decongestants and other nasal preparations for topical use;d07a corticosteroids, plain;d07x corticosteroids, other combinations;s02b corticosteroids;s03b corticosteroids;s01c antiinflammatory agents and antiinfectives in combination;s01b antiinflammatory agents;h02a corticosteroids for systemic use, plain;c05a agents for treatment of hemorrhoids and anal fissures for topical use
## 1477 n02a opioids;n01a anesthetics, general
## 1478 n02a opioids;n01a anesthetics, general
## 1479 g03c estrogens;l02a hormones and related agents
## 1480 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 1481 n06a antidepressants
## 1482 n02b other analgesics and antipyretics
## 1483 n05b anxiolytics
## 1484 n06a antidepressants
## 1485 n06a antidepressants
## 1486 <NA>
## 1487 n03a antiepileptics
## 1488 c02a antiadrenergic agents, centrally acting
## 1489 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 1490 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1491 n05b anxiolytics
## 1492 n05b anxiolytics
## 1493 v03a all other therapeutic products
## 1494 n05b anxiolytics
## 1495 r02a throat preparations;d04a antipruritics, incl. antihistamines, anesthetics, etc.;n01b anesthetics, local;s02d other otologicals;s01h local anesthetics;c05a agents for treatment of hemorrhoids and anal fissures for topical use;c01b antiarrhythmics, class i and iii
## 1496 a04a antiemetics and antinauseants;n05c hypnotics and sedatives;s01f mydriatics and cycloplegics
## 1497 n05b anxiolytics
## 1498 v03a all other therapeutic products
## 1499 n05b anxiolytics
## 1500 n06a antidepressants
## 1501 n06a antidepressants
## 1502 n06a antidepressants
## 1503 n05b anxiolytics
## 1504 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1505 n06a antidepressants
## 1506 g03c estrogens;l02a hormones and related agents
## 1507 n06a antidepressants
## 1508 n06a antidepressants
## 1509 <NA>
## 1510 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1511 b01a antithrombotic agents
## 1512 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 1513 n06a antidepressants
## 1514 a01a stomatological preparations;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d01a antifungals for topical use
## 1515 n06a antidepressants
## 1516 n06a antidepressants
## 1517 n06a antidepressants
## 1518 <NA>
## 1519 n06b psychostimulants, agents used for adhd and nootropics;n06b psychostimulants, agents used for adhd and nootropics
## 1520 n07b drugs used in addictive disorders
## 1521 n02a opioids
## 1522 n06a antidepressants
## 1523 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 1524 n06a antidepressants
## 1525 n06a antidepressants
## 1526 g03c estrogens;g03c estrogens
## 1527 a04a antiemetics and antinauseants
## 1528 n06a antidepressants
## 1529 n05a antipsychotics
## 1530 n06a antidepressants
## 1531 n06a antidepressants
## 1532 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 1533 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 1534 m04a antigout preparations
## 1535 <NA>
## 1536 j01e sulfonamides and trimethoprim
## 1537 j04a drugs for treatment of tuberculosis
## 1538 r03c adrenergics for systemic use
## 1539 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1540 n06a antidepressants
## 1541 n06a antidepressants
## 1542 n06a antidepressants
## 1543 n05b anxiolytics
## 1544 n05b anxiolytics
## 1545 r05d cough suppressants, excl. combinations with expectorants
## 1546 n02a opioids
## 1547 <NA>
## 1548 n06a antidepressants
## 1549 v03a all other therapeutic products
## 1550 j01f macrolides, lincosamides and streptogramins
## 1551 a04a antiemetics and antinauseants;n05c hypnotics and sedatives;s01f mydriatics and cycloplegics
## 1552 n03a antiepileptics;n03a antiepileptics
## 1553 a01a stomatological preparations;j01a tetracyclines;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d06a antibiotics for topical use;s01a antiinfectives
## 1554 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1555 n05a antipsychotics
## 1556 <NA>
## 1557 n06a antidepressants
## 1558 a10b blood glucose lowering drugs, excl. insulins
## 1559 c07a beta blocking agents
## 1560 c02a antiadrenergic agents, centrally acting
## 1561 n05b anxiolytics
## 1562 n07b drugs used in addictive disorders
## 1563 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 1564 a04a antiemetics and antinauseants;n05c hypnotics and sedatives;s01f mydriatics and cycloplegics
## 1565 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 1566 n03a antiepileptics
## 1567 c02a antiadrenergic agents, centrally acting
## 1568 g03a hormonal contraceptives for systemic use;g03d progestogens
## 1569 n05b anxiolytics
## 1570 n02a opioids
## 1571 a06a drugs for constipation;v03a all other therapeutic products
## 1572 n06a antidepressants
## 1573 n06a antidepressants
## 1574 n06a antidepressants
## 1575 n06a antidepressants
## 1576 n06a antidepressants
## 1577 n02a opioids
## 1578 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;s01b antiinflammatory agents;c01e other cardiac preparations
## 1579 a16a other alimentary tract and metabolism products
## 1580 n05a antipsychotics
## 1581 n05b anxiolytics
## 1582 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1583 n02a opioids;n01a anesthetics, general
## 1584 a01a stomatological preparations;j01a tetracyclines;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d06a antibiotics for topical use;s01a antiinfectives
## 1585 a14a anabolic steroids
## 1586 g03a hormonal contraceptives for systemic use;g03a hormonal contraceptives for systemic use
## 1587 n06a antidepressants
## 1588 n05b anxiolytics
## 1589 d04a antipruritics, incl. antihistamines, anesthetics, etc.;n06a antidepressants
## 1590 c08d selective calcium channel blockers with direct cardiac effects;c05a agents for treatment of hemorrhoids and anal fissures for topical use
## 1591 n06a antidepressants
## 1592 n01a anesthetics, general
## 1593 n01a anesthetics, general
## 1594 n01a anesthetics, general
## 1595 n06a antidepressants
## 1596 n06a antidepressants
## 1597 c01b antiarrhythmics, class i and iii
## 1598 c09a ace inhibitors, plain
## 1599 c10a lipid modifying agents, plain
## 1600 c08d selective calcium channel blockers with direct cardiac effects
## 1601 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 1602 <NA>
## 1603 n05b anxiolytics
## 1604 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 1605 n02b other analgesics and antipyretics
## 1606 n03a antiepileptics
## 1607 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 1608 n06a antidepressants
## 1609 n06a antidepressants
## 1610 g03c estrogens;l02a hormones and related agents
## 1611 j05a direct acting antivirals
## 1612 n03a antiepileptics;n03a antiepileptics;n03a antiepileptics
## 1613 a04a antiemetics and antinauseants;n05c hypnotics and sedatives;s01f mydriatics and cycloplegics
## 1614 n06a antidepressants
## 1615 n06d anti-dementia drugs
## 1616 a01a stomatological preparations
## 1617 g03c estrogens
## 1618 g03c estrogens
## 1619 a10b blood glucose lowering drugs, excl. insulins
## 1620 a10b blood glucose lowering drugs, excl. insulins
## 1621 n06a antidepressants
## 1622 n06a antidepressants
## 1623 n05b anxiolytics
## 1624 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 1625 n02b other analgesics and antipyretics;n02b other analgesics and antipyretics
## 1626 n06b psychostimulants, agents used for adhd and nootropics
## 1627 n06b psychostimulants, agents used for adhd and nootropics
## 1628 n06b psychostimulants, agents used for adhd and nootropics
## 1629 n06b psychostimulants, agents used for adhd and nootropics
## 1630 <NA>
## 1631 <NA>
## 1632 n03a antiepileptics
## 1633 n03a antiepileptics
## 1634 <NA>
## 1635 <NA>
## 1636 a01a stomatological preparations;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d01a antifungals for topical use
## 1637 a01a stomatological preparations;g01a antiinfectives and antiseptics, excl. combinations with corticosteroids;d01a antifungals for topical use
## 1638 d01b antifungals for systemic use;d01a antifungals for topical use
## 1639 d01b antifungals for systemic use;d01a antifungals for topical use
## 1640 c07a beta blocking agents
## 1641 c07a beta blocking agents
## 1642 a10b blood glucose lowering drugs, excl. insulins
## 1643 a10b blood glucose lowering drugs, excl. insulins
## 1644 a02b drugs for peptic ulcer and gastro-oesophageal reflux disease (gord)
## 1645 a02b drugs for peptic ulcer and gastro-oesophageal reflux disease (gord)
## 1646 c07a beta blocking agents
## 1647 c07a beta blocking agents
## 1648 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1649 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1650 n06a antidepressants
## 1651 n06a antidepressants
## 1652 n06a antidepressants
## 1653 n06a antidepressants
## 1654 n06a antidepressants
## 1655 n06a antidepressants
## 1656 n06a antidepressants
## 1657 n06a antidepressants
## 1658 n06a antidepressants
## 1659 n06a antidepressants
## 1660 n06a antidepressants
## 1661 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 1662 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;d11a other dermatological preparations;s01b antiinflammatory agents
## 1663 n06a antidepressants
## 1664 n06a antidepressants
## 1665 n06a antidepressants
## 1666 n06a antidepressants
## 1667 r02a throat preparations;d08a antiseptics and disinfectants;n01b anesthetics, local;c05b antivaricose therapy
## 1668 r02a throat preparations;d08a antiseptics and disinfectants;n01b anesthetics, local;c05b antivaricose therapy
## 1669 <NA>
## 1670 <NA>
## 1671 n06a antidepressants
## 1672 n06a antidepressants
## 1673 n06a antidepressants
## 1674 n06a antidepressants
## 1675 n06a antidepressants
## 1676 n06a antidepressants
## 1677 n05b anxiolytics
## 1678 n05b anxiolytics
## 1679 g03c estrogens;l02a hormones and related agents
## 1680 g03c estrogens;l02a hormones and related agents
## 1681 n06a antidepressants
## 1682 n06a antidepressants
## 1683 n06a antidepressants
## 1684 n06a antidepressants
## 1685 n06a antidepressants
## 1686 n06a antidepressants
## 1687 n06a antidepressants
## 1688 n06a antidepressants
## 1689 n06a antidepressants
## 1690 n06a antidepressants
## 1691 n06a antidepressants
## 1692 n06a antidepressants
## 1693 n06a antidepressants
## 1694 n06a antidepressants
## 1695 n06a antidepressants
## 1696 n06a antidepressants
## 1697 n06a antidepressants
## 1698 n06a antidepressants
## 1699 n06a antidepressants
## 1700 n06a antidepressants
## 1701 n06a antidepressants
## 1702 n05b anxiolytics
## 1703 n06a antidepressants
## 1704 n06a antidepressants
## 1705 n06a antidepressants
## 1706 n06a antidepressants
## 1707 n06a antidepressants
## 1708 n06a antidepressants
## 1709 n06a antidepressants
## 1710 n06a antidepressants
## 1711 n06a antidepressants
## 1712 l01a alkylating agents
## 1713 <NA>
## 1714 <NA>
## 1715 n06a antidepressants
## 1716 n06a antidepressants
## 1717 n06a antidepressants
## 1718 n06a antidepressants
## 1719 n06a antidepressants
## 1720 n06a antidepressants
## 1721 n06a antidepressants
## 1722 n06a antidepressants
## 1723 n06a antidepressants
## 1724 n06a antidepressants
## 1725 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 1726 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 1727 n05c hypnotics and sedatives
## 1728 n02c antimigraine preparations;s01e antiglaucoma preparations and miotics;c02a antiadrenergic agents, centrally acting
## 1729 b02b vitamin k and other hemostatics;a01a stomatological preparations;r03a adrenergics, inhalants;r01a decongestants and other nasal preparations for topical use;s01e antiglaucoma preparations and miotics;c01c cardiac stimulants excl. cardiac glycosides
## 1730 c01c cardiac stimulants excl. cardiac glycosides
## 1731 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids;g02c other gynecologicals;r02a throat preparations;c01e other cardiac preparations
## 1732 n06a antidepressants
## 1733 n06a antidepressants
## 1734 n05a antipsychotics
## 1735 n05c hypnotics and sedatives
## 1736 d11a other dermatological preparations;n06b psychostimulants, agents used for adhd and nootropics
## 1737 j01e sulfonamides and trimethoprim
## 1738 j01m quinolone antibacterials [dg:dg01550];s01a antiinfectives
## 1739 n02a opioids
## 1740 m02a topical products for joint and muscular pain;m01a antiinflammatory and antirheumatic products, non-steroids
## species_name species_class behav_cat
## 1 Danio rerio Actinopterygii Boldness
## 2 Lampsilis fasciola Bivalvia Movement
## 3 Danio rerio Actinopterygii Boldness
## 4 Danio rerio Actinopterygii Boldness
## 5 Danio rerio Actinopterygii Boldness
## 6 Danio rerio Actinopterygii Boldness
## 7 Danio rerio Actinopterygii Boldness
## 8 Danio rerio Actinopterygii Movement
## 9 Danio rerio Actinopterygii Movement
## 10 Pimephales promelas Actinopterygii Boldness
## 11 Danio rerio Actinopterygii Movement
## 12 Perca fluviatilis Actinopterygii Foraging
## 13 Pimephales promelas Actinopterygii Boldness
## 14 Danio rerio Actinopterygii Boldness
## 15 Nothobranchius furzeri Actinopterygii Boldness
## 16 Danio rerio Actinopterygii Movement
## 17 Poecilia reticulata Actinopterygii Boldness
## 18 Apostichopus japonicus Holothuroidea Movement
## 19 Danio rerio Actinopterygii Boldness
## 20 Corbicula fluminea Bivalvia Foraging
## 21 Gambusia holbrooki Actinopterygii Mating
## 22 Danio rerio Actinopterygii Movement
## 23 Danio rerio Actinopterygii Boldness
## 24 Danio rerio Actinopterygii Boldness
## 25 Danio rerio Actinopterygii Boldness
## 26 Danio rerio Actinopterygii Boldness
## 27 Danio rerio Actinopterygii Movement
## 28 Danio rerio Actinopterygii Movement
## 29 Danio rerio Actinopterygii Boldness
## 30 Danio rerio Actinopterygii Movement
## 31 Danio rerio Actinopterygii Movement
## 32 Danio rerio Actinopterygii Movement
## 33 Danio rerio Actinopterygii Movement
## 34 Danio rerio Actinopterygii Movement
## 35 Danio rerio Actinopterygii Movement
## 36 Danio rerio Actinopterygii Movement
## 37 Danio rerio Actinopterygii Movement
## 38 Danio rerio Actinopterygii Movement
## 39 Danio rerio Actinopterygii Movement
## 40 Danio rerio Actinopterygii Movement
## 41 Danio rerio Actinopterygii Movement
## 42 Danio rerio Actinopterygii Movement
## 43 Poecilia reticulata Actinopterygii Boldness
## 44 Poecilia reticulata Actinopterygii Boldness
## 45 Tritonia diomedea Gastropoda Movement
## 46 Danio rerio Actinopterygii Boldness
## 47 Danio rerio Actinopterygii Boldness
## 48 Danio rerio Actinopterygii Boldness
## 49 Danio rerio Actinopterygii Boldness
## 50 Danio rerio Actinopterygii Boldness
## 51 Danio rerio Actinopterygii Boldness
## 52 Danio rerio Actinopterygii Boldness
## 53 Danio rerio Actinopterygii Boldness
## 54 Danio rerio Actinopterygii Movement
## 55 Danio rerio Actinopterygii Movement
## 56 Saccostrea glomerata Bivalvia Movement
## 57 Faxonius virilis Malacostraca Agression
## 58 Poecilia reticulata Actinopterygii Movement
## 59 Daphnia magna Branchiopoda Boldness
## 60 Lepomis gibbosus Actinopterygii Boldness
## 61 Lepomis gibbosus Actinopterygii Boldness
## 62 Poecilia reticulata Actinopterygii Movement
## 63 Clarias gariepinus Actinopterygii Movement
## 64 Pomatoschistus minutus Actinopterygii Mating
## 65 Betta splendens Actinopterygii Boldness
## 66 Danio rerio Actinopterygii Boldness
## 67 Danio rerio Actinopterygii Boldness
## 68 Pomatoschistus microps Actinopterygii Foraging
## 69 Danio rerio Actinopterygii Movement
## 70 Labroides dimidiatus Actinopterygii Sociality
## 71 Danio rerio Actinopterygii Boldness
## 72 Danio rerio Actinopterygii Boldness
## 73 Danio rerio Actinopterygii Movement
## 74 Pimephales promelas Actinopterygii Antipredator
## 75 Pimephales promelas Actinopterygii Antipredator
## 76 Pimephales promelas Actinopterygii Antipredator
## 77 Betta splendens Actinopterygii Boldness
## 78 Daphnia magna Branchiopoda Movement
## 79 Daphnia magna Branchiopoda Movement
## 80 Pimephales promelas Actinopterygii Mating
## 81 Danio rerio Actinopterygii Movement
## 82 Danio rerio Actinopterygii Boldness
## 83 Salmo trutta Actinopterygii Movement
## 84 Hediste diversicolor Polychaeta Not categorised
## 85 Danio rerio Actinopterygii Boldness
## 86 Daphnia magna Branchiopoda Foraging
## 87 Daphnia magna Branchiopoda Foraging
## 88 Sclerophrys arabica Amphibia Antipredator
## 89 Danio rerio Actinopterygii Mating
## 90 Danio rerio Actinopterygii Boldness
## 91 Danio rerio Actinopterygii Agression
## 92 Danio rerio Actinopterygii Movement
## 93 Danio rerio Actinopterygii Cognition
## 94 Betta splendens Actinopterygii Boldness
## 95 Xenopus laevis Amphibia Not categorised
## 96 Danio rerio Actinopterygii Boldness
## 97 Betta splendens Actinopterygii Agression
## 98 Danio rerio Actinopterygii Boldness
## 99 Danio rerio Actinopterygii Movement
## 100 Danio rerio Actinopterygii Movement
## 101 Danio rerio Actinopterygii Movement
## 102 Danio rerio Actinopterygii Movement
## 103 Danio rerio Actinopterygii Movement
## 104 Danio rerio Actinopterygii Boldness
## 105 Danio rerio Actinopterygii Movement
## 106 Oryzias latipes Actinopterygii Boldness
## 107 Danio rerio Actinopterygii Boldness
## 108 Danio rerio Actinopterygii Boldness
## 109 Danio rerio Actinopterygii Boldness
## 110 Danio rerio Actinopterygii Boldness
## 111 Danio rerio Actinopterygii Boldness
## 112 Danio rerio Actinopterygii Boldness
## 113 Morone saxatilis x morone chrysops Actinopterygii Foraging
## 114 Danio rerio Actinopterygii Boldness
## 115 Danio rerio Actinopterygii Boldness
## 116 Danio rerio Actinopterygii Boldness
## 117 Danio rerio Actinopterygii Boldness
## 118 Danio rerio Actinopterygii Boldness
## 119 Danio rerio Actinopterygii Movement
## 120 Danio rerio Actinopterygii Movement
## 121 Fundulus heteroclitus Actinopterygii Movement
## 122 Danio rerio Actinopterygii Mating
## 123 Daphnia magna Branchiopoda Movement
## 124 Danio rerio Actinopterygii Cognition
## 125 Danio rerio Actinopterygii Movement
## 126 Danio rerio Actinopterygii Movement
## 127 Danio rerio Actinopterygii Movement
## 128 Homarus americanus Malacostraca Movement
## 129 Homarus americanus Malacostraca Movement
## 130 Danio rerio Actinopterygii Boldness
## 131 Daphnia magna Branchiopoda Movement
## 132 Danio rerio Actinopterygii Movement
## 133 Danio rerio Actinopterygii Movement
## 134 Betta splendens Actinopterygii Mating
## 135 Syngnathus abaster Actinopterygii Antipredator
## 136 Danio rerio Actinopterygii Movement
## 137 Danio rerio Actinopterygii Movement
## 138 Pimephales promelas Actinopterygii Antipredator
## 139 Salmo trutta Actinopterygii Foraging
## 140 Coenagrion hastulatum Insecta Antipredator
## 141 Microsternarchus bilineatus Actinopterygii Not categorised
## 142 Microsternarchus bilineatus Actinopterygii Not categorised
## 143 Microsternarchus bilineatus Actinopterygii Not categorised
## 144 Danio rerio Actinopterygii Movement
## 145 Danio rerio Actinopterygii Movement
## 146 Danio rerio Actinopterygii Movement
## 147 Danio rerio Actinopterygii Movement
## 148 Danio rerio Actinopterygii Movement
## 149 Danio rerio Actinopterygii Boldness
## 150 Danio rerio Actinopterygii Boldness
## 151 Danio rerio Actinopterygii Boldness
## 152 Danio rerio Actinopterygii Boldness
## 153 Danio rerio Actinopterygii Boldness
## 154 Danio rerio Actinopterygii Boldness
## 155 Danio rerio Actinopterygii Boldness
## 156 Danio rerio Actinopterygii Boldness
## 157 Danio rerio Actinopterygii Boldness
## 158 Danio rerio Actinopterygii Boldness
## 159 Danio rerio Actinopterygii Boldness
## 160 Danio rerio Actinopterygii Movement
## 161 Astyanax mexicanus Actinopterygii Sociality
## 162 Danio rerio Actinopterygii Boldness
## 163 Danio rerio Actinopterygii Boldness
## 164 Danio rerio Actinopterygii Boldness
## 165 Danio rerio Actinopterygii Movement
## 166 Danio rerio Actinopterygii Movement
## 167 Pimephales promelas Actinopterygii Antipredator
## 168 Pimephales promelas Actinopterygii Antipredator
## 169 Perca fluviatilis Actinopterygii Boldness
## 170 Danio rerio Actinopterygii Boldness
## 171 Danio rerio Actinopterygii Boldness
## 172 Lepomis gibbosus Actinopterygii Boldness
## 173 Lepomis gibbosus Actinopterygii Boldness
## 174 Lepomis gibbosus Actinopterygii Boldness
## 175 Amphiprion ocellaris Actinopterygii Agression
## 176 Gasterosteus aculeatus Actinopterygii Mating
## 177 Oryzias latipes Actinopterygii Movement
## 178 Danio rerio Actinopterygii Boldness
## 179 Danio rerio Actinopterygii Boldness
## 180 Danio rerio Actinopterygii Boldness
## 181 Danio rerio Actinopterygii Boldness
## 182 Danio rerio Actinopterygii Movement
## 183 Danio rerio Actinopterygii Movement
## 184 Gasterosteus aculeatus Actinopterygii Boldness
## 185 Danio rerio Actinopterygii Movement
## 186 Danio rerio Actinopterygii Movement
## 187 Danio rerio Actinopterygii Movement
## 188 Danio rerio Actinopterygii Movement
## 189 Sepia officinalis Cephalopoda Not categorised
## 190 Danio rerio Actinopterygii Boldness
## 191 Danio rerio Actinopterygii Boldness
## 192 Danio rerio Actinopterygii Boldness
## 193 Danio rerio Actinopterygii Boldness
## 194 Danio rerio Actinopterygii Boldness
## 195 Danio rerio Actinopterygii Boldness
## 196 Danio rerio Actinopterygii Boldness
## 197 Danio rerio Actinopterygii Movement
## 198 Gammarus spp Malacostraca Foraging
## 199 Gammarus spp Malacostraca Movement
## 200 Xenopus tropicalis Amphibia Mating
## 201 Danio rerio Actinopterygii Movement
## 202 Danio rerio Actinopterygii Movement
## 203 Danio rerio Actinopterygii Movement
## 204 Danio rerio Actinopterygii Movement
## 205 Danio rerio Actinopterygii Movement
## 206 Danio rerio Actinopterygii Movement
## 207 Danio rerio Actinopterygii Movement
## 208 Danio rerio Actinopterygii Movement
## 209 Danio rerio Actinopterygii Movement
## 210 Danio rerio Actinopterygii Movement
## 211 Danio rerio Actinopterygii Movement
## 212 Danio rerio Actinopterygii Movement
## 213 Danio rerio Actinopterygii Movement
## 214 Danio rerio Actinopterygii Movement
## 215 Danio rerio Actinopterygii Movement
## 216 Danio rerio Actinopterygii Movement
## 217 Danio rerio Actinopterygii Movement
## 218 Danio rerio Actinopterygii Movement
## 219 Danio rerio Actinopterygii Movement
## 220 Danio rerio Actinopterygii Movement
## 221 Danio rerio Actinopterygii Movement
## 222 Danio rerio Actinopterygii Movement
## 223 Danio rerio Actinopterygii Mating
## 224 Danio rerio Actinopterygii Boldness
## 225 Danio rerio Actinopterygii Boldness
## 226 Danio rerio Actinopterygii Boldness
## 227 Danio rerio Actinopterygii Boldness
## 228 Danio rerio Actinopterygii Boldness
## 229 Danio rerio Actinopterygii Boldness
## 230 Danio rerio Actinopterygii Boldness
## 231 Betta splendens Actinopterygii Agression
## 232 Pimephales promelas Actinopterygii Antipredator
## 233 Pimephales promelas Actinopterygii Antipredator
## 234 Pimephales promelas Actinopterygii Antipredator
## 235 Pimephales promelas Actinopterygii Antipredator
## 236 Schmidtea mediterranea Rhabditophora Movement
## 237 Danio rerio Actinopterygii Movement
## 238 Danio rerio Actinopterygii Movement
## 239 Danio rerio Actinopterygii Movement
## 240 Danio rerio Actinopterygii Movement
## 241 Danio rerio Actinopterygii Movement
## 242 Danio rerio Actinopterygii Movement
## 243 Danio rerio Actinopterygii Movement
## 244 Danio rerio Actinopterygii Movement
## 245 Danio rerio Actinopterygii Movement
## 246 Salmo salar Actinopterygii Movement
## 247 Pomatoschistus microps Actinopterygii Foraging
## 248 Danio rerio Actinopterygii Not categorised
## 249 Danio rerio Actinopterygii Not categorised
## 250 Betta splendens Actinopterygii Boldness
## 251 Gammarus pulex Malacostraca Movement
## 252 Gammarus pulex Malacostraca Boldness
## 253 Daphnia magna Branchiopoda Movement
## 254 Daphnia magna Branchiopoda Movement
## 255 Daphnia magna Branchiopoda Movement
## 256 Daphnia magna Branchiopoda Movement
## 257 Daphnia magna Branchiopoda Movement
## 258 Daphnia magna Branchiopoda Movement
## 259 Daphnia magna Branchiopoda Movement
## 260 Daphnia magna Branchiopoda Movement
## 261 Daphnia magna Branchiopoda Movement
## 262 Daphnia magna Branchiopoda Movement
## 263 Daphnia magna Branchiopoda Movement
## 264 Sepia officinalis Cephalopoda Foraging
## 265 Oncorhynchus mykiss Actinopterygii Movement
## 266 Betta splendens Actinopterygii Mating
## 267 Danio rerio Actinopterygii Movement
## 268 Poecilia reticulata Actinopterygii Mating
## 269 Gambusia holbrooki Actinopterygii Boldness
## 270 Echinogammarus marinus Malacostraca Boldness
## 271 Echinogammarus marinus Malacostraca Boldness
## 272 Leporinus macrocephalus Actinopterygii Antipredator
## 273 Poecilia reticulata Actinopterygii Boldness
## 274 Danio rerio Actinopterygii Boldness
## 275 Danio rerio Actinopterygii Boldness
## 276 Danio rerio Actinopterygii Boldness
## 277 Danio rerio Actinopterygii Movement
## 278 Danio rerio Actinopterygii Movement
## 279 Danio rerio Actinopterygii Movement
## 280 Danio rerio Actinopterygii Movement
## 281 Danio rerio Actinopterygii Movement
## 282 Betta splendens Actinopterygii Mating
## 283 Danio rerio Actinopterygii Movement
## 284 Danio rerio Actinopterygii Movement
## 285 Daphnia magna Branchiopoda Movement
## 286 Danio rerio Actinopterygii Not categorised
## 287 Danio rerio Actinopterygii Not categorised
## 288 Danio rerio Actinopterygii Not categorised
## 289 Danio rerio Actinopterygii Movement
## 290 Gasterosteus aculeatus Actinopterygii Boldness
## 291 Danio rerio Actinopterygii Movement
## 292 Danio rerio Actinopterygii Movement
## 293 Danio rerio Actinopterygii Movement
## 294 Danio rerio Actinopterygii Movement
## 295 Danio rerio Actinopterygii Movement
## 296 Danio rerio Actinopterygii Movement
## 297 Danio rerio Actinopterygii Movement
## 298 Danio rerio Actinopterygii Boldness
## 299 Radix balthica Gastropoda Movement
## 300 Danio rerio Actinopterygii Boldness
## 301 Danio rerio Actinopterygii Boldness
## 302 Danio rerio Actinopterygii Boldness
## 303 Danio rerio Actinopterygii Boldness
## 304 Lampsilis fasciola Bivalvia Movement
## 305 Diacyclops belgicus Copepoda Movement
## 306 Salmo trutta Actinopterygii Movement
## 307 Betta splendens Actinopterygii Movement
## 308 Pacifastacus leniusculus Malacostraca Movement
## 309 Schmidtea mediterranea Rhabditophora Movement
## 310 Schmidtea mediterranea Rhabditophora Movement
## 311 Carassius auratus Actinopterygii Movement
## 312 Carassius auratus Actinopterygii Movement
## 313 Danio rerio Actinopterygii Movement
## 314 Danio rerio Actinopterygii Movement
## 315 Danio rerio Actinopterygii Movement
## 316 Danio rerio Actinopterygii Movement
## 317 Danio rerio Actinopterygii Boldness
## 318 Danio rerio Actinopterygii Boldness
## 319 Danio rerio Actinopterygii Boldness
## 320 Danio rerio Actinopterygii Boldness
## 321 Danio rerio Actinopterygii Boldness
## 322 Danio rerio Actinopterygii Movement
## 323 Danio rerio Actinopterygii Movement
## 324 Danio rerio Actinopterygii Movement
## 325 Danio rerio Actinopterygii Movement
## 326 Danio rerio Actinopterygii Movement
## 327 Danio rerio Actinopterygii Movement
## 328 Danio rerio Actinopterygii Movement
## 329 Danio rerio Actinopterygii Movement
## 330 Danio rerio Actinopterygii Movement
## 331 Danio rerio Actinopterygii Movement
## 332 Danio rerio Actinopterygii Movement
## 333 Danio rerio Actinopterygii Movement
## 334 Danio rerio Actinopterygii Movement
## 335 Danio rerio Actinopterygii Movement
## 336 Danio rerio Actinopterygii Movement
## 337 Danio rerio Actinopterygii Movement
## 338 Danio rerio Actinopterygii Movement
## 339 Dugesia dorotocephala Rhabditophora Movement
## 340 Dugesia dorotocephala Rhabditophora Movement
## 341 Dugesia dorotocephala Rhabditophora Movement
## 342 Dugesia dorotocephala Rhabditophora Movement
## 343 Procambarus virginalis Malacostraca Movement
## 344 Procambarus virginalis Malacostraca Movement
## 345 Danio rerio Actinopterygii Sociality
## 346 Daphnia magna Branchiopoda Foraging
## 347 Danio rerio Actinopterygii Boldness
## 348 Danio rerio Actinopterygii Boldness
## 349 Danio rerio Actinopterygii Boldness
## 350 Danio rerio Actinopterygii Boldness
## 351 Danio rerio Actinopterygii Boldness
## 352 Radix balthica Gastropoda Movement
## 353 Labroides dimidiatus Actinopterygii Boldness
## 354 Carassius auratus Actinopterygii Movement
## 355 Danio rerio Actinopterygii Boldness
## 356 Danio rerio Actinopterygii Boldness
## 357 Danio rerio Actinopterygii Boldness
## 358 Danio rerio Actinopterygii Movement
## 359 Opsanus beta Actinopterygii Agression
## 360 Danio rerio Actinopterygii Movement
## 361 Danio rerio Actinopterygii Boldness
## 362 Orthopristis chrysopterus Actinopterygii Foraging
## 363 Oryzias latipes Actinopterygii Movement
## 364 Oryzias latipes Actinopterygii Movement
## 365 Mytilus galloprovincialis Bivalvia Foraging
## 366 Mytilus galloprovincialis Bivalvia Foraging
## 367 Danio rerio Actinopterygii Movement
## 368 Danio rerio Actinopterygii Movement
## 369 Danio rerio Actinopterygii Movement
## 370 Danio rerio Actinopterygii Movement
## 371 Danio rerio Actinopterygii Movement
## 372 Danio rerio Actinopterygii Movement
## 373 Danio rerio Actinopterygii Movement
## 374 Danio rerio Actinopterygii Movement
## 375 Danio rerio Actinopterygii Movement
## 376 Danio rerio Actinopterygii Movement
## 377 Danio rerio Actinopterygii Movement
## 378 Danio rerio Actinopterygii Movement
## 379 Danio rerio Actinopterygii Movement
## 380 Clonorchis sinensis Trematoda Movement
## 381 Clonorchis sinensis Trematoda Movement
## 382 Clonorchis sinensis Trematoda Movement
## 383 Danio rerio Actinopterygii Boldness
## 384 Danio rerio Actinopterygii Boldness
## 385 Danio rerio Actinopterygii Boldness
## 386 Danio rerio Actinopterygii Boldness
## 387 Gasterosteus aculeatus Actinopterygii Boldness
## 388 Syngnathus scovelli Actinopterygii Mating
## 389 Poecilia reticulata Actinopterygii Mating
## 390 Oncorhynchus mykiss Actinopterygii Movement
## 391 Oncorhynchus mykiss Actinopterygii Movement
## 392 Oncorhynchus mykiss Actinopterygii Movement
## 393 Branchipodopsis wolfi Branchiopoda Boldness
## 394 Neogobius melanostomus Actinopterygii Movement
## 395 Danio rerio Actinopterygii Sociality
## 396 Danio rerio Actinopterygii Sociality
## 397 Danio rerio Actinopterygii Sociality
## 398 Danio rerio Actinopterygii Boldness
## 399 Danio rerio Actinopterygii Boldness
## 400 Carassius auratus Actinopterygii Boldness
## 401 Carassius auratus Actinopterygii Boldness
## 402 Diplodus sargus Actinopterygii Movement
## 403 Jenynsia multidentata Actinopterygii Mating
## 404 Poecilia reticulata Actinopterygii Mating
## 405 Procambarus virginalis Malacostraca Movement
## 406 Procambarus virginalis Malacostraca Movement
## 407 Daphnia spp Branchiopoda Movement
## 408 Xenopus laevis Amphibia Cognition
## 409 Danio rerio Actinopterygii Foraging
## 410 Gasterosteus aculeatus Actinopterygii Mating
## 411 Danio rerio Actinopterygii Antipredator
## 412 Daphnia magna Branchiopoda Foraging
## 413 Xenopus laevis Amphibia Movement
## 414 Xenopus laevis Amphibia Movement
## 415 Poecilia reticulata Actinopterygii Mating
## 416 Perca fluviatilis Actinopterygii Boldness
## 417 Astyanax mexicanus Actinopterygii Boldness
## 418 Biomphalaria glabrata Gastropoda Mating
## 419 Biomphalaria glabrata Gastropoda Mating
## 420 Biomphalaria glabrata Gastropoda Mating
## 421 Biomphalaria glabrata Gastropoda Mating
## 422 Poecilia reticulata Actinopterygii Mating
## 423 Danio rerio Actinopterygii Boldness
## 424 Danio rerio Actinopterygii Boldness
## 425 Danio rerio Actinopterygii Boldness
## 426 Danio rerio Actinopterygii Boldness
## 427 Danio rerio Actinopterygii Movement
## 428 Betta splendens Actinopterygii Mating
## 429 Danio rerio Actinopterygii Movement
## 430 Danio rerio Actinopterygii Movement
## 431 Danio rerio Actinopterygii Movement
## 432 Danio rerio Actinopterygii Movement
## 433 Zygoptera spp Insecta Movement
## 434 Zygoptera spp Insecta Movement
## 435 Oryzias latipes Actinopterygii Mating
## 436 Danio rerio Actinopterygii Boldness
## 437 Danio rerio Actinopterygii Boldness
## 438 Danio rerio Actinopterygii Boldness
## 439 Danio rerio Actinopterygii Movement
## 440 Danio rerio Actinopterygii Movement
## 441 Danio rerio Actinopterygii Movement
## 442 Danio rerio Actinopterygii Movement
## 443 Danio rerio Actinopterygii Movement
## 444 Nothobranchius furzeri Actinopterygii Boldness
## 445 Danio rerio Actinopterygii Movement
## 446 Stenonema spp Insecta Movement
## 447 Danio rerio Actinopterygii Movement
## 448 Danio rerio Actinopterygii Movement
## 449 Danio rerio Actinopterygii Movement
## 450 Danio rerio Actinopterygii Movement
## 451 Danio rerio Actinopterygii Movement
## 452 Danio rerio Actinopterygii Boldness
## 453 Danio rerio Actinopterygii Movement
## 454 Danio rerio Actinopterygii Movement
## 455 Danio rerio Actinopterygii Movement
## 456 Danio rerio Actinopterygii Movement
## 457 Danio rerio Actinopterygii Movement
## 458 Danio rerio Actinopterygii Movement
## 459 Danio rerio Actinopterygii Movement
## 460 Danio rerio Actinopterygii Movement
## 461 Danio rerio Actinopterygii Movement
## 462 Argyrosomus regius Actinopterygii Boldness
## 463 Danio rerio Actinopterygii Cognition
## 464 Carassius auratus Actinopterygii Cognition
## 465 Pimephales promelas Actinopterygii Movement
## 466 Lampsilis siliquoidea Bivalvia Foraging
## 467 Lampsilis siliquoidea Bivalvia Foraging
## 468 Lampsilis siliquoidea Bivalvia Foraging
## 469 Salmo trutta Actinopterygii Not categorised
## 470 Danio rerio Actinopterygii Movement
## 471 Betta splendens Actinopterygii Movement
## 472 Oncorhynchus mykiss Actinopterygii Not categorised
## 473 Pimephales promelas Actinopterygii Antipredator
## 474 Pimephales promelas Actinopterygii Antipredator
## 475 Pimephales promelas Actinopterygii Antipredator
## 476 Pimephales promelas Actinopterygii Movement
## 477 Danio rerio Actinopterygii Not categorised
## 478 Lampsilis siliquoidea Bivalvia Foraging
## 479 Lampsilis siliquoidea Bivalvia Foraging
## 480 Hediste diversicolor Polychaeta Foraging
## 481 Pacifastacus leniusculus Malacostraca Movement
## 482 Betta splendens Actinopterygii Mating
## 483 Danio rerio Actinopterygii Movement
## 484 Colossoma macropomum Actinopterygii Movement
## 485 Colossoma macropomum Actinopterygii Movement
## 486 Daphnia magna Branchiopoda Foraging
## 487 Lymnaea stagnalis Gastropoda Cognition
## 488 Echinogammarus marinus Malacostraca Movement
## 489 Echinogammarus marinus Malacostraca Movement
## 490 Echinogammarus marinus Malacostraca Movement
## 491 Corbicula fluminea Bivalvia Foraging
## 492 Danio rerio Actinopterygii Boldness
## 493 Danio rerio Actinopterygii Movement
## 494 Danio rerio Actinopterygii Mating
## 495 Gambusia affinis Actinopterygii Agression
## 496 Danio rerio Actinopterygii Cognition
## 497 Danio rerio Actinopterygii Cognition
## 498 Danio rerio Actinopterygii Cognition
## 499 Physa elliptica Gastropoda Movement
## 500 Physa elliptica Gastropoda Movement
## 501 Physa elliptica Gastropoda Movement
## 502 Physa elliptica Gastropoda Movement
## 503 Physa elliptica Gastropoda Movement
## 504 Physa elliptica Gastropoda Movement
## 505 Danio rerio Actinopterygii Movement
## 506 Danio rerio Actinopterygii Cognition
## 507 Danio rerio Actinopterygii Cognition
## 508 Salmo trutta Actinopterygii Movement
## 509 Danio rerio Actinopterygii Movement
## 510 Danio rerio Actinopterygii Movement
## 511 Danio rerio Actinopterygii Movement
## 512 Danio rerio Actinopterygii Movement
## 513 Danio rerio Actinopterygii Movement
## 514 Danio rerio Actinopterygii Movement
## 515 Danio rerio Actinopterygii Movement
## 516 Danio rerio Actinopterygii Movement
## 517 Danio rerio Actinopterygii Movement
## 518 Danio rerio Actinopterygii Movement
## 519 Danio rerio Actinopterygii Movement
## 520 Danio rerio Actinopterygii Movement
## 521 Danio rerio Actinopterygii Movement
## 522 Danio rerio Actinopterygii Movement
## 523 Danio rerio Actinopterygii Movement
## 524 Danio rerio Actinopterygii Movement
## 525 Danio rerio Actinopterygii Movement
## 526 Danio rerio Actinopterygii Movement
## 527 Danio rerio Actinopterygii Movement
## 528 Danio rerio Actinopterygii Movement
## 529 Danio rerio Actinopterygii Movement
## 530 Danio rerio Actinopterygii Movement
## 531 Danio rerio Actinopterygii Movement
## 532 Danio rerio Actinopterygii Movement
## 533 Danio rerio Actinopterygii Movement
## 534 Betta splendens Actinopterygii Movement
## 535 Oncorhynchus mykiss Actinopterygii Not categorised
## 536 Betta splendens Actinopterygii Movement
## 537 Jenynsia multidentata Actinopterygii Boldness
## 538 Salmo trutta Actinopterygii Boldness
## 539 Pimephales promelas Actinopterygii Foraging
## 540 Gasterosteus aculeatus Actinopterygii Foraging
## 541 Gambusia holbrooki Actinopterygii Foraging
## 542 Danio rerio Actinopterygii Movement
## 543 Danio rerio Actinopterygii Movement
## 544 Danio rerio Actinopterygii Movement
## 545 Danio rerio Actinopterygii Movement
## 546 Daphnia magna Branchiopoda Foraging
## 547 Daphnia magna Branchiopoda Foraging
## 548 Procambarus clarkii Malacostraca Boldness
## 549 Danio rerio Actinopterygii Boldness
## 550 Danio rerio Actinopterygii Boldness
## 551 Salmo salar Actinopterygii Boldness
## 552 Betta splendens Actinopterygii Agression
## 553 Squalius cephalus Actinopterygii Boldness
## 554 Danio rerio Actinopterygii Movement
## 555 Neohelice granulata Malacostraca Cognition
## 556 Danio rerio Actinopterygii Movement
## 557 Danio rerio Actinopterygii Cognition
## 558 Gambusia affinis Actinopterygii Movement
## 559 Gambusia holbrooki Actinopterygii Mating
## 560 Danio rerio Actinopterygii Boldness
## 561 Danio rerio Actinopterygii Agression
## 562 Danio rerio Actinopterygii Boldness
## 563 Danio rerio Actinopterygii Boldness
## 564 Bufo bufo Amphibia Movement
## 565 Bufo bufo Amphibia Movement
## 566 Xenopus laevis Amphibia Mating
## 567 Xenopus laevis Amphibia Mating
## 568 Carassius auratus Actinopterygii Foraging
## 569 Danio rerio Actinopterygii Movement
## 570 Gambusia holbrooki Actinopterygii Mating
## 571 Danio rerio Actinopterygii Boldness
## 572 Danio rerio Actinopterygii Movement
## 573 Danio rerio Actinopterygii Movement
## 574 Rutilus rutilus Actinopterygii Foraging
## 575 Tachysurus fulvidraco Actinopterygii Foraging
## 576 Danio rerio Actinopterygii Boldness
## 577 Danio rerio Actinopterygii Boldness
## 578 Betta splendens Actinopterygii Mating
## 579 Danio rerio Actinopterygii Movement
## 580 Danio rerio Actinopterygii Movement
## 581 Danio rerio Actinopterygii Movement
## 582 Danio rerio Actinopterygii Movement
## 583 Daphnia magna Branchiopoda Foraging
## 584 Oncorhynchus mykiss Actinopterygii Foraging
## 585 Salmo salar Actinopterygii Movement
## 586 Danio rerio Actinopterygii Boldness
## 587 Danio rerio Actinopterygii Boldness
## 588 Daphnia magna Branchiopoda Movement
## 589 Danio rerio Actinopterygii Movement
## 590 Danio rerio Actinopterygii Movement
## 591 Danio rerio Actinopterygii Movement
## 592 Gambusia holbrooki Actinopterygii Antipredator
## 593 Squalius cephalus Actinopterygii Foraging
## 594 Ilyanassa obsoleta Gastropoda Movement
## 595 Ilyanassa obsoleta Gastropoda Movement
## 596 Ilyanassa obsoleta Gastropoda Movement
## 597 Ilyanassa obsoleta Gastropoda Movement
## 598 Ilyanassa obsoleta Gastropoda Movement
## 599 Cyprinodon variegatus Actinopterygii Movement
## 600 Pimephales promelas Actinopterygii Mating
## 601 Danio rerio Actinopterygii Boldness
## 602 Danio rerio Actinopterygii Boldness
## 603 Danio rerio Actinopterygii Boldness
## 604 Danio rerio Actinopterygii Boldness
## 605 Oryzias latipes Actinopterygii Movement
## 606 Daphnia magna Branchiopoda Movement
## 607 Daphnia magna Branchiopoda Movement
## 608 Danio rerio Actinopterygii Boldness
## 609 Capitella teleta Polychaeta Foraging
## 610 Sepia officinalis Cephalopoda Movement
## 611 Clarias gariepinus Actinopterygii Movement
## 612 Danio rerio Actinopterygii Movement
## 613 Danio rerio Actinopterygii Movement
## 614 Sepia officinalis Cephalopoda Boldness
## 615 Syngnathus abaster Actinopterygii Movement
## 616 Danio rerio Actinopterygii Movement
## 617 Danio rerio Actinopterygii Movement
## 618 Danio rerio Actinopterygii Movement
## 619 Danio rerio Actinopterygii Movement
## 620 Gambusia affinis Actinopterygii Mating
## 621 Planorbarius corneus Gastropoda Movement
## 622 Pimephales promelas Actinopterygii Foraging
## 623 Gammarus pulex Malacostraca Movement
## 624 Rutilus rutilus Actinopterygii Boldness
## 625 Danio rerio Actinopterygii Movement
## 626 Carassius auratus Actinopterygii Mating
## 627 Homarus gammarus Malacostraca Movement
## 628 Perca fluviatilis Actinopterygii Antipredator
## 629 Perca fluviatilis Actinopterygii Antipredator
## 630 Perca fluviatilis Actinopterygii Antipredator
## 631 Danio rerio Actinopterygii Boldness
## 632 Danio rerio Actinopterygii Movement
## 633 Betta splendens Actinopterygii Mating
## 634 Danio rerio Actinopterygii Movement
## 635 Danio rerio Actinopterygii Movement
## 636 Danio rerio Actinopterygii Movement
## 637 Danio rerio Actinopterygii Movement
## 638 Danio rerio Actinopterygii Movement
## 639 Danio rerio Actinopterygii Movement
## 640 Sparus aurata Actinopterygii Movement
## 641 Morone saxatilis x morone chrysops Actinopterygii Foraging
## 642 Danio rerio Actinopterygii Movement
## 643 Daphnia magna Branchiopoda Foraging
## 644 Faxonius virilis Malacostraca Antipredator
## 645 Ceriodaphnia cornuta Branchiopoda Antipredator
## 646 Danio rerio Actinopterygii Movement
## 647 Danio rerio Actinopterygii Movement
## 648 Danio rerio Actinopterygii Movement
## 649 Danio rerio Actinopterygii Movement
## 650 Danio rerio Actinopterygii Movement
## 651 Danio rerio Actinopterygii Movement
## 652 Gasterosteus aculeatus Actinopterygii Agression
## 653 Gasterosteus aculeatus Actinopterygii Boldness
## 654 Gasterosteus aculeatus Actinopterygii Antipredator
## 655 Danio rerio Actinopterygii Boldness
## 656 Cyprinus carpio Actinopterygii Movement
## 657 Cyprinus carpio Actinopterygii Movement
## 658 Poecilia reticulata Actinopterygii Movement
## 659 Danio rerio Actinopterygii Movement
## 660 Danio rerio Actinopterygii Movement
## 661 Danio rerio Actinopterygii Movement
## 662 Danio rerio Actinopterygii Movement
## 663 Danio rerio Actinopterygii Movement
## 664 Danio rerio Actinopterygii Movement
## 665 Danio rerio Actinopterygii Movement
## 666 Danio rerio Actinopterygii Movement
## 667 Danio rerio Actinopterygii Movement
## 668 Danio rerio Actinopterygii Movement
## 669 Danio rerio Actinopterygii Movement
## 670 Danio rerio Actinopterygii Movement
## 671 Danio rerio Actinopterygii Movement
## 672 Danio rerio Actinopterygii Movement
## 673 Danio rerio Actinopterygii Movement
## 674 Danio rerio Actinopterygii Movement
## 675 Danio rerio Actinopterygii Movement
## 676 Danio rerio Actinopterygii Movement
## 677 Poecilia reticulata Actinopterygii Mating
## 678 Gasterosteus aculeatus Actinopterygii Boldness
## 679 Gasterosteus aculeatus Actinopterygii Boldness
## 680 Daphnia magna Branchiopoda Movement
## 681 Danio rerio Actinopterygii Movement
## 682 Lampsilis fasciola Bivalvia Movement
## 683 Danio rerio Actinopterygii Boldness
## 684 Danio rerio Actinopterygii Boldness
## 685 Lampsilis fasciola Bivalvia Foraging
## 686 Lampsilis fasciola Bivalvia Foraging
## 687 Squalius cephalus Actinopterygii Agression
## 688 Squalius cephalus Actinopterygii Agression
## 689 Squalius cephalus Actinopterygii Agression
## 690 Squalius cephalus Actinopterygii Agression
## 691 Perca fluviatilis Actinopterygii Boldness
## 692 Perca fluviatilis Actinopterygii Boldness
## 693 Perca fluviatilis Actinopterygii Boldness
## 694 Perca fluviatilis Actinopterygii Boldness
## 695 Daphnia magna Branchiopoda Movement
## 696 Danio rerio Actinopterygii Movement
## 697 Carassius auratus Actinopterygii Foraging
## 698 Gammarus pulex Malacostraca Movement
## 699 Gammarus pulex Malacostraca Movement
## 700 Gammarus pulex Malacostraca Movement
## 701 Pimephales promelas Actinopterygii Boldness
## 702 Pimephales promelas Actinopterygii Boldness
## 703 Danio rerio Actinopterygii Movement
## 704 Gambusia holbrooki Actinopterygii Boldness
## 705 Gambusia holbrooki Actinopterygii Movement
## 706 Pomatoschistus minutus Actinopterygii Mating
## 707 Danio rerio Actinopterygii Mating
## 708 Danio rerio Actinopterygii Boldness
## 709 Danio rerio Actinopterygii Movement
## 710 Danio rerio Actinopterygii Movement
## 711 Danio rerio Actinopterygii Boldness
## 712 Danio rerio Actinopterygii Movement
## 713 Danio rerio Actinopterygii Movement
## 714 Danio rerio Actinopterygii Movement
## 715 Dugesia dorotocephala Rhabditophora Movement
## 716 Danio rerio Actinopterygii Movement
## 717 Pimephales promelas Actinopterygii Mating
## 718 Danio rerio Actinopterygii Boldness
## 719 Danio rerio Actinopterygii Boldness
## 720 Daphnia magna Branchiopoda Movement
## 721 Daphnia magna Branchiopoda Movement
## 722 Danio rerio Actinopterygii Boldness
## 723 Oncorhynchus mykiss Actinopterygii Boldness
## 724 Clarias gariepinus Actinopterygii Movement
## 725 Oryzias latipes Actinopterygii Movement
## 726 Danio rerio Actinopterygii Movement
## 727 Taricha granulosa Amphibia Movement
## 728 Danio rerio Actinopterygii Boldness
## 729 Danio rerio Actinopterygii Boldness
## 730 Danio rerio Actinopterygii Movement
## 731 Danio rerio Actinopterygii Movement
## 732 Danio rerio Actinopterygii Movement
## 733 Danio rerio Actinopterygii Movement
## 734 Danio rerio Actinopterygii Movement
## 735 Danio rerio Actinopterygii Movement
## 736 Danio rerio Actinopterygii Movement
## 737 Danio rerio Actinopterygii Movement
## 738 Faxonius virilis Malacostraca Agression
## 739 Carcinus maenas Malacostraca Movement
## 740 Syngnathus scovelli Actinopterygii Mating
## 741 Oreochromis niloticus Actinopterygii Foraging
## 742 Carassius carassius Actinopterygii Boldness
## 743 Carassius carassius Actinopterygii Boldness
## 744 Salmo salar Actinopterygii Mating
## 745 Danio rerio Actinopterygii Movement
## 746 Salmo trutta Actinopterygii Movement
## 747 Salmo trutta Actinopterygii Movement
## 748 Danio rerio Actinopterygii Boldness
## 749 Danio rerio Actinopterygii Boldness
## 750 Danio rerio Actinopterygii Boldness
## 751 Danio rerio Actinopterygii Boldness
## 752 Danio rerio Actinopterygii Boldness
## 753 Danio rerio Actinopterygii Boldness
## 754 Danio rerio Actinopterygii Boldness
## 755 Danio rerio Actinopterygii Boldness
## 756 Danio rerio Actinopterygii Boldness
## 757 Danio rerio Actinopterygii Boldness
## 758 Danio rerio Actinopterygii Boldness
## 759 Danio rerio Actinopterygii Boldness
## 760 Danio rerio Actinopterygii Movement
## 761 Danio rerio Actinopterygii Movement
## 762 Xenopus laevis Amphibia Mating
## 763 Danio rerio Actinopterygii Movement
## 764 Danio rerio Actinopterygii Movement
## 765 Danio rerio Actinopterygii Movement
## 766 Danio rerio Actinopterygii Movement
## 767 Danio rerio Actinopterygii Movement
## 768 Danio rerio Actinopterygii Movement
## 769 Pimephales promelas Actinopterygii Mating
## 770 Pimephales promelas Actinopterygii Mating
## 771 Daphnia magna Branchiopoda Movement
## 772 Pimephales promelas Actinopterygii Antipredator
## 773 Pimephales promelas Actinopterygii Antipredator
## 774 Pimephales promelas Actinopterygii Antipredator
## 775 Bryocamptus pygmaeus Copepoda Movement
## 776 Dugesia tigrina Rhabditophora Movement
## 777 Dugesia tigrina Rhabditophora Movement
## 778 Pimephales promelas Actinopterygii Mating
## 779 Danio rerio Actinopterygii Boldness
## 780 Salmo trutta fario Actinopterygii Movement
## 781 Gambusia holbrooki Actinopterygii Boldness
## 782 Danio rerio Actinopterygii Boldness
## 783 Danio rerio Actinopterygii Boldness
## 784 Betta splendens Actinopterygii Agression
## 785 Oryzias latipes Actinopterygii Boldness
## 786 Perca fluviatilis Actinopterygii Boldness
## 787 Danio rerio Actinopterygii Boldness
## 788 Danio rerio Actinopterygii Boldness
## 789 Danio rerio Actinopterygii Boldness
## 790 Pomatoschistus minutus Actinopterygii Mating
## 791 Oncorhynchus mykiss Actinopterygii Movement
## 792 Danio rerio Actinopterygii Boldness
## 793 Danio rerio Actinopterygii Boldness
## 794 Danio rerio Actinopterygii Boldness
## 795 Danio rerio Actinopterygii Boldness
## 796 Danio rerio Actinopterygii Boldness
## 797 Danio rerio Actinopterygii Sociality
## 798 Danio rerio Actinopterygii Sociality
## 799 Aeshna cyanea Insecta Foraging
## 800 Aeshna cyanea Insecta Foraging
## 801 Danio rerio Actinopterygii Boldness
## 802 Danio rerio Actinopterygii Movement
## 803 Jenynsia multidentata Actinopterygii Mating
## 804 Amatitlania nigrofasciata Actinopterygii Boldness
## 805 Danio rerio Actinopterygii Boldness
## 806 Danio rerio Actinopterygii Movement
## 807 Danio rerio Actinopterygii Movement
## 808 Amatitlania nigrofasciata Actinopterygii Mating
## 809 Oncorhynchus mykiss Actinopterygii Agression
## 810 Oncorhynchus mykiss Actinopterygii Agression
## 811 Danio rerio Actinopterygii Boldness
## 812 Danio rerio Actinopterygii Boldness
## 813 Carassius auratus Actinopterygii Movement
## 814 Carassius auratus Actinopterygii Movement
## 815 Danio rerio Actinopterygii Movement
## 816 Danio rerio Actinopterygii Movement
## 817 Danio rerio Actinopterygii Movement
## 818 Danio rerio Actinopterygii Movement
## 819 Danio rerio Actinopterygii Movement
## 820 Danio rerio Actinopterygii Movement
## 821 Danio rerio Actinopterygii Movement
## 822 Danio rerio Actinopterygii Movement
## 823 Heliocidaris crassispina Echinoidea Movement
## 824 Micropterus salmoides Actinopterygii Movement
## 825 Micropterus salmoides Actinopterygii Movement
## 826 Sepia officinalis Cephalopoda Foraging
## 827 Danio rerio Actinopterygii Boldness
## 828 Danio rerio Actinopterygii Boldness
## 829 Danio rerio Actinopterygii Boldness
## 830 Danio rerio Actinopterygii Boldness
## 831 Danio rerio Actinopterygii Boldness
## 832 Oryzias latipes Actinopterygii Mating
## 833 Danio rerio Actinopterygii Boldness
## 834 Danio rerio Actinopterygii Boldness
## 835 Daphnia magna Branchiopoda Movement
## 836 Daphnia magna Branchiopoda Movement
## 837 Danio rerio Actinopterygii Boldness
## 838 Danio rerio Actinopterygii Boldness
## 839 Cyprinodon variegatus Actinopterygii Movement
## 840 Carcinus maenas Malacostraca Boldness
## 841 Betta splendens Actinopterygii Agression
## 842 Danio rerio Actinopterygii Movement
## 843 Danio rerio Actinopterygii Movement
## 844 Danio rerio Actinopterygii Movement
## 845 Danio rerio Actinopterygii Movement
## 846 Danio rerio Actinopterygii Movement
## 847 Danio rerio Actinopterygii Movement
## 848 Danio rerio Actinopterygii Movement
## 849 Danio rerio Actinopterygii Foraging
## 850 Carassius auratus Actinopterygii Movement
## 851 Danio rerio Actinopterygii Movement
## 852 Danio rerio Actinopterygii Movement
## 853 Danio rerio Actinopterygii Movement
## 854 Danio rerio Actinopterygii Movement
## 855 Danio rerio Actinopterygii Movement
## 856 Danio rerio Actinopterygii Cognition
## 857 Danio rerio Actinopterygii Cognition
## 858 Aphanius dispar Actinopterygii Movement
## 859 Carassius carassius Actinopterygii Boldness
## 860 Danio rerio Actinopterygii Boldness
## 861 Danio rerio Actinopterygii Boldness
## 862 Phalloceros harpagos Actinopterygii Movement
## 863 Phalloceros harpagos Actinopterygii Movement
## 864 Carassius auratus Actinopterygii Movement
## 865 Hydra vulgaris Hydrozoa Foraging
## 866 Hydra vulgaris Hydrozoa Foraging
## 867 Hydra vulgaris Hydrozoa Foraging
## 868 Hydra vulgaris Hydrozoa Foraging
## 869 Hydra vulgaris Hydrozoa Foraging
## 870 Hydra vulgaris Hydrozoa Foraging
## 871 Hydra vulgaris Hydrozoa Foraging
## 872 Hydra vulgaris Hydrozoa Foraging
## 873 Hydra vulgaris Hydrozoa Foraging
## 874 Hydra vulgaris Hydrozoa Foraging
## 875 Morone saxatilis x morone chrysops Actinopterygii Foraging
## 876 Danio rerio Actinopterygii Boldness
## 877 Lymnaea stagnalis Gastropoda Movement
## 878 Oryzias latipes Actinopterygii Movement
## 879 Oryzias latipes Actinopterygii Movement
## 880 Oryzias latipes Actinopterygii Movement
## 881 Oryzias latipes Actinopterygii Movement
## 882 Oryzias latipes Actinopterygii Movement
## 883 Oryzias latipes Actinopterygii Movement
## 884 Carassius auratus Actinopterygii Foraging
## 885 Danio rerio Actinopterygii Sociality
## 886 Danio rerio Actinopterygii Movement
## 887 Danio rerio Actinopterygii Movement
## 888 Xenopus laevis Amphibia Mating
## 889 Danio rerio Actinopterygii Movement
## 890 Gasterosteus aculeatus Actinopterygii Mating
## 891 Danio rerio Actinopterygii Movement
## 892 Poecilia reticulata Actinopterygii Antipredator
## 893 Danio rerio Actinopterygii Movement
## 894 Danio rerio Actinopterygii Movement
## 895 Daphnia magna Branchiopoda Movement
## 896 Daphnia magna Branchiopoda Movement
## 897 Daphnia magna Branchiopoda Movement
## 898 Daphnia magna Branchiopoda Movement
## 899 Danio rerio Actinopterygii Sociality
## 900 Oncorhynchus mykiss Actinopterygii Foraging
## 901 Carassius auratus Actinopterygii Movement
## 902 Danio rerio Actinopterygii Movement
## 903 Leporinus macrocephalus Actinopterygii Antipredator
## 904 Procambarus clarkii Malacostraca Boldness
## 905 Oncorhynchus mykiss Actinopterygii Movement
## 906 Danio rerio Actinopterygii Movement
## 907 Danio rerio Actinopterygii Movement
## 908 Danio rerio Actinopterygii Movement
## 909 Danio rerio Actinopterygii Movement
## 910 Danio rerio Actinopterygii Movement
## 911 Danio rerio Actinopterygii Movement
## 912 Danio rerio Actinopterygii Movement
## 913 Danio rerio Actinopterygii Movement
## 914 Danio rerio Actinopterygii Movement
## 915 Procambarus virginalis Malacostraca Movement
## 916 Procambarus virginalis Malacostraca Movement
## 917 Danio rerio Actinopterygii Movement
## 918 Gasterosteus aculeatus Actinopterygii Mating
## 919 Pimephales promelas Actinopterygii Foraging
## 920 Hirudo verbana Clitellata Movement
## 921 Danio rerio Actinopterygii Mating
## 922 Danio rerio Actinopterygii Mating
## 923 Danio rerio Actinopterygii Movement
## 924 Danio rerio Actinopterygii Movement
## 925 Danio rerio Actinopterygii Movement
## 926 Danio rerio Actinopterygii Movement
## 927 Cyprinella lutrensis Actinopterygii Mating
## 928 Danio rerio Actinopterygii Boldness
## 929 Clarias gariepinus Actinopterygii Movement
## 930 Danio rerio Actinopterygii Boldness
## 931 Danio rerio Actinopterygii Boldness
## 932 Danio rerio Actinopterygii Boldness
## 933 Danio rerio Actinopterygii Boldness
## 934 Danio rerio Actinopterygii Boldness
## 935 Danio rerio Actinopterygii Boldness
## 936 Danio rerio Actinopterygii Boldness
## 937 Danio rerio Actinopterygii Boldness
## 938 Orconectes rusticus Malacostraca Boldness
## 939 Poecilia reticulata Actinopterygii Mating
## 940 Pimephales promelas Actinopterygii Boldness
## 941 Oryzias latipes Actinopterygii Movement
## 942 Danio rerio Actinopterygii Movement
## 943 Danio rerio Actinopterygii Movement
## 944 Danio rerio Actinopterygii Movement
## 945 Danio rerio Actinopterygii Movement
## 946 Oryzias latipes Actinopterygii Mating
## 947 Danio rerio Actinopterygii Boldness
## 948 Danio rerio Actinopterygii Boldness
## 949 Danio rerio Actinopterygii Boldness
## 950 Gambusia holbrooki Actinopterygii Boldness
## 951 Danio rerio Actinopterygii Movement
## 952 Danio rerio Actinopterygii Boldness
## 953 Sclerophrys arabica Amphibia Antipredator
## 954 Sclerophrys arabica Amphibia Antipredator
## 955 Sclerophrys arabica Amphibia Antipredator
## 956 Hydra viridissima Hydrozoa Foraging
## 957 Pimephales promelas Actinopterygii Mating
## 958 Daphnia magna Branchiopoda Foraging
## 959 Danio rerio Actinopterygii Movement
## 960 Danio rerio Actinopterygii Movement
## 961 Danio rerio Actinopterygii Movement
## 962 Danio rerio Actinopterygii Movement
## 963 Danio rerio Actinopterygii Movement
## 964 Danio rerio Actinopterygii Movement
## 965 Danio rerio Actinopterygii Movement
## 966 Danio rerio Actinopterygii Movement
## 967 Danio rerio Actinopterygii Movement
## 968 Danio rerio Actinopterygii Movement
## 969 Danio rerio Actinopterygii Boldness
## 970 Danio rerio Actinopterygii Movement
## 971 Danio rerio Actinopterygii Movement
## 972 Danio rerio Actinopterygii Boldness
## 973 Nothobranchius furzeri Actinopterygii Mating
## 974 Daphnia magna Branchiopoda Movement
## 975 Daphnia magna Branchiopoda Movement
## 976 Daphnia magna Branchiopoda Movement
## 977 Daphnia magna Branchiopoda Movement
## 978 Danio rerio Actinopterygii Movement
## 979 Danio rerio Actinopterygii Movement
## 980 Rana catesbeiana Amphibia Antipredator
## 981 Rana catesbeiana Amphibia Antipredator
## 982 Rana catesbeiana Amphibia Antipredator
## 983 Danio rerio Actinopterygii Boldness
## 984 Danio rerio Actinopterygii Boldness
## 985 Danio rerio Actinopterygii Boldness
## 986 Danio rerio Actinopterygii Boldness
## 987 Danio rerio Actinopterygii Movement
## 988 Danio rerio Actinopterygii Boldness
## 989 Danio rerio Actinopterygii Boldness
## 990 Poecilia wingei Actinopterygii Mating
## 991 Danio rerio Actinopterygii Boldness
## 992 Danio rerio Actinopterygii Movement
## 993 Danio rerio Actinopterygii Movement
## 994 Hemigrapsus oregonensis Malacostraca Movement
## 995 Corbicula fluminea Bivalvia Foraging
## 996 Danio rerio Actinopterygii Boldness
## 997 Dugesia dorotocephala Rhabditophora Boldness
## 998 Dugesia dorotocephala Rhabditophora Boldness
## 999 Dugesia dorotocephala Rhabditophora Boldness
## 1000 Dugesia dorotocephala Rhabditophora Boldness
## 1001 Dugesia dorotocephala Rhabditophora Boldness
## 1002 Danio rerio Actinopterygii Movement
## 1003 Poecilia reticulata Actinopterygii Mating
## 1004 Danio rerio Actinopterygii Movement
## 1005 Danio rerio Actinopterygii Movement
## 1006 Danio rerio Actinopterygii Movement
## 1007 Danio rerio Actinopterygii Movement
## 1008 Danio rerio Actinopterygii Movement
## 1009 Danio rerio Actinopterygii Movement
## 1010 Danio rerio Actinopterygii Movement
## 1011 Danio rerio Actinopterygii Movement
## 1012 Danio rerio Actinopterygii Movement
## 1013 Danio rerio Actinopterygii Movement
## 1014 Danio rerio Actinopterygii Movement
## 1015 Danio rerio Actinopterygii Movement
## 1016 Danio rerio Actinopterygii Boldness
## 1017 Danio rerio Actinopterygii Boldness
## 1018 Cyprinus carpio Actinopterygii Movement
## 1019 Cyprinus carpio Actinopterygii Movement
## 1020 Cyprinus carpio Actinopterygii Movement
## 1021 Gambusia holbrooki Actinopterygii Movement
## 1022 Gambusia holbrooki Actinopterygii Movement
## 1023 Danio rerio Actinopterygii Movement
## 1024 Procambarus clarkii Malacostraca Not categorised
## 1025 Procambarus clarkii Malacostraca Not categorised
## 1026 Poecilia reticulata Actinopterygii Movement
## 1027 Danio rerio Actinopterygii Movement
## 1028 Danio rerio Actinopterygii Mating
## 1029 Danio rerio Actinopterygii Movement
## 1030 Trachemys scripta Reptilia Movement
## 1031 Danio rerio Actinopterygii Boldness
## 1032 Danio rerio Actinopterygii Boldness
## 1033 Danio rerio Actinopterygii Boldness
## 1034 Danio rerio Actinopterygii Boldness
## 1035 Danio rerio Actinopterygii Boldness
## 1036 Danio rerio Actinopterygii Boldness
## 1037 Danio rerio Actinopterygii Boldness
## 1038 Danio rerio Actinopterygii Boldness
## 1039 Faxonius limosus Malacostraca Boldness
## 1040 Danio rerio Actinopterygii Movement
## 1041 Danio rerio Actinopterygii Foraging
## 1042 Danio rerio Actinopterygii Boldness
## 1043 Dugesia dorotocephala Rhabditophora Movement
## 1044 Dugesia dorotocephala Rhabditophora Movement
## 1045 Dugesia dorotocephala Rhabditophora Movement
## 1046 Danio rerio Actinopterygii Movement
## 1047 Lates calcarifer Actinopterygii Not categorised
## 1048 Danio rerio Actinopterygii Movement
## 1049 Danio rerio Actinopterygii Movement
## 1050 Danio rerio Actinopterygii Movement
## 1051 Danio rerio Actinopterygii Movement
## 1052 Danio rerio Actinopterygii Movement
## 1053 Danio rerio Actinopterygii Movement
## 1054 Danio rerio Actinopterygii Movement
## 1055 Danio rerio Actinopterygii Movement
## 1056 Danio rerio Actinopterygii Movement
## 1057 Danio rerio Actinopterygii Movement
## 1058 Danio rerio Actinopterygii Movement
## 1059 Danio rerio Actinopterygii Movement
## 1060 Danio rerio Actinopterygii Movement
## 1061 Danio rerio Actinopterygii Movement
## 1062 Danio rerio Actinopterygii Boldness
## 1063 Danio rerio Actinopterygii Boldness
## 1064 Danio rerio Actinopterygii Boldness
## 1065 Danio rerio Actinopterygii Boldness
## 1066 Danio rerio Actinopterygii Movement
## 1067 Danio rerio Actinopterygii Movement
## 1068 Danio rerio Actinopterygii Movement
## 1069 Danio rerio Actinopterygii Movement
## 1070 Danio rerio Actinopterygii Movement
## 1071 Danio rerio Actinopterygii Movement
## 1072 Danio rerio Actinopterygii Movement
## 1073 Danio rerio Actinopterygii Movement
## 1074 Danio rerio Actinopterygii Movement
## 1075 Danio rerio Actinopterygii Mating
## 1076 Danio rerio Actinopterygii Movement
## 1077 Hyla cinerea Amphibia Movement
## 1078 Oryzias latipes Actinopterygii Foraging
## 1079 Oryzias latipes Actinopterygii Foraging
## 1080 Gambusia holbrooki Actinopterygii Boldness
## 1081 Mytilus spp Bivalvia Foraging
## 1082 Mytilus spp Bivalvia Foraging
## 1083 Atyaephyra desmarestii Malacostraca Foraging
## 1084 Atyaephyra desmarestii Malacostraca Foraging
## 1085 Atyaephyra desmarestii Malacostraca Foraging
## 1086 Danio rerio Actinopterygii Movement
## 1087 Danio rerio Actinopterygii Movement
## 1088 Danio rerio Actinopterygii Movement
## 1089 Danio rerio Actinopterygii Movement
## 1090 Diamesa zernyi Insecta Movement
## 1091 Diamesa zernyi Insecta Movement
## 1092 Diamesa zernyi Insecta Movement
## 1093 Danio rerio Actinopterygii Boldness
## 1094 Oncorhynchus mykiss Actinopterygii Boldness
## 1095 Danio rerio Actinopterygii Movement
## 1096 Poecilia reticulata Actinopterygii Mating
## 1097 Danio rerio Actinopterygii Movement
## 1098 Danio rerio Actinopterygii Movement
## 1099 Procambarus clarkii Malacostraca Boldness
## 1100 Daphnia magna Branchiopoda Foraging
## 1101 Lithobates pipiens Amphibia Not categorised
## 1102 Lithobates pipiens Amphibia Not categorised
## 1103 Danio rerio Actinopterygii Boldness
## 1104 Danio rerio Actinopterygii Boldness
## 1105 Danio rerio Actinopterygii Boldness
## 1106 Danio rerio Actinopterygii Movement
## 1107 Danio rerio Actinopterygii Movement
## 1108 Danio rerio Actinopterygii Movement
## 1109 Chrysemys picta Reptilia Cognition
## 1110 Hippocampus erectus Actinopterygii Foraging
## 1111 Hippocampus erectus Actinopterygii Foraging
## 1112 Danio rerio Actinopterygii Movement
## 1113 Danio rerio Actinopterygii Movement
## 1114 Danio rerio Actinopterygii Movement
## 1115 Danio rerio Actinopterygii Movement
## 1116 Poecilia reticulata Actinopterygii Mating
## 1117 Oryzias latipes Actinopterygii Sociality
## 1118 Oryzias latipes Actinopterygii Sociality
## 1119 Pimephales promelas Actinopterygii Not categorised
## 1120 Danio rerio Actinopterygii Boldness
## 1121 Danio rerio Actinopterygii Boldness
## 1122 Danio rerio Actinopterygii Boldness
## 1123 Danio rerio Actinopterygii Movement
## 1124 Danio rerio Actinopterygii Movement
## 1125 Danio rerio Actinopterygii Movement
## 1126 Danio rerio Actinopterygii Movement
## 1127 Danio rerio Actinopterygii Movement
## 1128 Danio rerio Actinopterygii Movement
## 1129 Poecilia reticulata Actinopterygii Boldness
## 1130 Carcinus maenas Malacostraca Boldness
## 1131 Carassius auratus Actinopterygii Mating
## 1132 Danio rerio Actinopterygii Movement
## 1133 Poecilia reticulata Actinopterygii Movement
## 1134 Danio rerio Actinopterygii Movement
## 1135 Danio rerio Actinopterygii Movement
## 1136 Danio rerio Actinopterygii Movement
## 1137 Astyanax mexicanus Actinopterygii Movement
## 1138 Astyanax mexicanus Actinopterygii Movement
## 1139 Astyanax mexicanus Actinopterygii Movement
## 1140 Astyanax mexicanus Actinopterygii Movement
## 1141 Astyanax mexicanus Actinopterygii Movement
## 1142 Cichlasoma dimerus Actinopterygii Foraging
## 1143 Danio rerio Actinopterygii Boldness
## 1144 Danio rerio Actinopterygii Boldness
## 1145 Danio rerio Actinopterygii Boldness
## 1146 Danio rerio Actinopterygii Boldness
## 1147 Danio rerio Actinopterygii Movement
## 1148 Danio rerio Actinopterygii Movement
## 1149 Gasterosteus aculeatus Actinopterygii Antipredator
## 1150 Danio rerio Actinopterygii Movement
## 1151 Gymnotus carapo Actinopterygii Movement
## 1152 Gymnotus carapo Actinopterygii Movement
## 1153 Gymnotus carapo Actinopterygii Movement
## 1154 Gymnotus carapo Actinopterygii Movement
## 1155 Pimephales promelas Actinopterygii Mating
## 1156 Betta splendens Actinopterygii Movement
## 1157 Danio rerio Actinopterygii Boldness
## 1158 Danio rerio Actinopterygii Boldness
## 1159 Danio rerio Actinopterygii Boldness
## 1160 Betta splendens Actinopterygii Mating
## 1161 Poecilia reticulata Actinopterygii Boldness
## 1162 Orconectes rusticus Malacostraca Movement
## 1163 Oryzias latipes Actinopterygii Movement
## 1164 Oryzias latipes Actinopterygii Movement
## 1165 Danio rerio Actinopterygii Movement
## 1166 Danio rerio Actinopterygii Movement
## 1167 Danio rerio Actinopterygii Movement
## 1168 Danio rerio Actinopterygii Movement
## 1169 Gasterosteus aculeatus Actinopterygii Movement
## 1170 Lithobates pipiens Amphibia Movement
## 1171 Lithobates pipiens Amphibia Movement
## 1172 Daphnia magna Branchiopoda Movement
## 1173 Daphnia magna Branchiopoda Movement
## 1174 Daphnia magna Branchiopoda Movement
## 1175 Poecilia reticulata Actinopterygii Movement
## 1176 Perca fluviatilis Actinopterygii Movement
## 1177 Daphnia magna Branchiopoda Movement
## 1178 Poecilia reticulata Actinopterygii Movement
## 1179 Brachionus calyciflorus Monogononta Foraging
## 1180 Danio rerio Actinopterygii Movement
## 1181 Heteropneustes fossilis Actinopterygii Movement
## 1182 Orconectes rusticus Malacostraca Cognition
## 1183 Orconectes rusticus Malacostraca Movement
## 1184 Orconectes rusticus Malacostraca Movement
## 1185 Orconectes rusticus Malacostraca Movement
## 1186 Amphiprion ocellaris Actinopterygii Movement
## 1187 Danio rerio Actinopterygii Movement
## 1188 Eumetopias jubatus Mammalia Movement
## 1189 Eumetopias jubatus Mammalia Movement
## 1190 Danio rerio Actinopterygii Boldness
## 1191 Danio rerio Actinopterygii Movement
## 1192 Danio rerio Actinopterygii Movement
## 1193 Danio rerio Actinopterygii Movement
## 1194 Danio rerio Actinopterygii Movement
## 1195 Danio rerio Actinopterygii Movement
## 1196 Salmo salar Actinopterygii Movement
## 1197 Daphnia magna Branchiopoda Movement
## 1198 Carassius auratus Actinopterygii Movement
## 1199 Danio rerio Actinopterygii Movement
## 1200 Danio rerio Actinopterygii Movement
## 1201 Danio rerio Actinopterygii Movement
## 1202 Xenopus laevis Amphibia Mating
## 1203 Xenopus laevis Amphibia Mating
## 1204 Danio rerio Actinopterygii Boldness
## 1205 Gambusia holbrooki Actinopterygii Movement
## 1206 Thalassoma bifasciatum Actinopterygii Movement
## 1207 Oreochromis niloticus Actinopterygii Agression
## 1208 Danio rerio Actinopterygii Boldness
## 1209 Danio rerio Actinopterygii Boldness
## 1210 Poecilia wingei Actinopterygii Boldness
## 1211 Danio rerio Actinopterygii Boldness
## 1212 Danio rerio Actinopterygii Boldness
## 1213 Danio rerio Actinopterygii Boldness
## 1214 Danio rerio Actinopterygii Boldness
## 1215 Betta splendens Actinopterygii Agression
## 1216 Danio rerio Actinopterygii Movement
## 1217 Danio rerio Actinopterygii Mating
## 1218 Pomacea lineata Gastropoda Movement
## 1219 Gammarus locusta Malacostraca Movement
## 1220 Cyprinus carpio Actinopterygii Not categorised
## 1221 Cyprinus carpio Actinopterygii Not categorised
## 1222 Cyprinus carpio Actinopterygii Not categorised
## 1223 Cyprinus carpio Actinopterygii Not categorised
## 1224 Danio rerio Actinopterygii Boldness
## 1225 Danio rerio Actinopterygii Boldness
## 1226 Poecilia reticulata Actinopterygii Mating
## 1227 Poecilia reticulata Actinopterygii Movement
## 1228 Danio rerio Actinopterygii Movement
## 1229 Pimephales promelas Actinopterygii Antipredator
## 1230 Oryzias melastigma Actinopterygii Movement
## 1231 Danio rerio Actinopterygii Boldness
## 1232 Danio rerio Actinopterygii Movement
## 1233 Danio rerio Actinopterygii Movement
## 1234 Danio rerio Actinopterygii Boldness
## 1235 Danio rerio Actinopterygii Boldness
## 1236 Danio rerio Actinopterygii Boldness
## 1237 Procambarus clarkii Malacostraca Boldness
## 1238 Oryzias javanicus Actinopterygii Boldness
## 1239 Danio rerio Actinopterygii Movement
## 1240 Dugesia tigrina Rhabditophora Movement
## 1241 Daphnia magna Branchiopoda Movement
## 1242 Pimephales promelas Actinopterygii Post mating
## 1243 Daphnia pulex Branchiopoda Movement
## 1244 Daphnia pulex Branchiopoda Movement
## 1245 Danio rerio Actinopterygii Movement
## 1246 Danio rerio Actinopterygii Movement
## 1247 Danio rerio Actinopterygii Movement
## 1248 Sepia officinalis Cephalopoda Not categorised
## 1249 Danio rerio Actinopterygii Movement
## 1250 Dugesia dorotocephala Rhabditophora Movement
## 1251 Dugesia dorotocephala Rhabditophora Movement
## 1252 Dugesia dorotocephala Rhabditophora Movement
## 1253 Dugesia dorotocephala Rhabditophora Movement
## 1254 Danio rerio Actinopterygii Movement
## 1255 Dugesia spp Rhabditophora Movement
## 1256 Danio rerio Actinopterygii Movement
## 1257 Danio rerio Actinopterygii Movement
## 1258 Danio rerio Actinopterygii Movement
## 1259 Danio rerio Actinopterygii Movement
## 1260 Danio rerio Actinopterygii Movement
## 1261 Gambusia holbrooki Actinopterygii Movement
## 1262 Gambusia holbrooki Actinopterygii Movement
## 1263 Gambusia holbrooki Actinopterygii Movement
## 1264 Danio rerio Actinopterygii Movement
## 1265 Danio rerio Actinopterygii Movement
## 1266 Rhamdia quelen Actinopterygii Boldness
## 1267 Danio rerio Actinopterygii Boldness
## 1268 Pachygrapsus crassipes Malacostraca Boldness
## 1269 Nothobranchius furzeri Actinopterygii Boldness
## 1270 Gammarus fossarum Malacostraca Foraging
## 1271 Danio rerio Actinopterygii Movement
## 1272 Danio rerio Actinopterygii Movement
## 1273 Pimephales promelas Actinopterygii Mating
## 1274 Prochilodus lineatus Actinopterygii Movement
## 1275 Danio rerio Actinopterygii Movement
## 1276 Gambusia holbrooki Actinopterygii Mating
## 1277 Gambusia holbrooki Actinopterygii Mating
## 1278 Sparus aurata Actinopterygii Movement
## 1279 Danio rerio Actinopterygii Boldness
## 1280 Danio rerio Actinopterygii Movement
## 1281 Danio rerio Actinopterygii Movement
## 1282 Salmo salar Actinopterygii Movement
## 1283 Danio rerio Actinopterygii Foraging
## 1284 Danio rerio Actinopterygii Movement
## 1285 Xenopus laevis Amphibia Boldness
## 1286 Xenopus laevis Amphibia Boldness
## 1287 Xenopus laevis Amphibia Boldness
## 1288 Oryzias latipes Actinopterygii Boldness
## 1289 Danio rerio Actinopterygii Movement
## 1290 Gambusia affinis Actinopterygii Mating
## 1291 Hediste diversicolor Polychaeta Movement
## 1292 Danio rerio Actinopterygii Movement
## 1293 Danio rerio Actinopterygii Movement
## 1294 Danio rerio Actinopterygii Movement
## 1295 Danio rerio Actinopterygii Movement
## 1296 Daphnia magna Branchiopoda Movement
## 1297 Daphnia magna Branchiopoda Movement
## 1298 Daphnia magna Branchiopoda Movement
## 1299 Daphnia magna Branchiopoda Movement
## 1300 Daphnia magna Branchiopoda Movement
## 1301 Daphnia magna Branchiopoda Movement
## 1302 Daphnia magna Branchiopoda Movement
## 1303 Daphnia magna Branchiopoda Movement
## 1304 Daphnia magna Branchiopoda Movement
## 1305 Daphnia magna Branchiopoda Movement
## 1306 Fundulus heteroclitus Actinopterygii Movement
## 1307 Perca fluviatilis Actinopterygii Movement
## 1308 Daphnia magna Branchiopoda Movement
## 1309 Daphnia magna Branchiopoda Movement
## 1310 Daphnia magna Branchiopoda Movement
## 1311 Daphnia magna Branchiopoda Movement
## 1312 Daphnia magna Branchiopoda Movement
## 1313 Daphnia magna Branchiopoda Movement
## 1314 Daphnia magna Branchiopoda Movement
## 1315 Daphnia magna Branchiopoda Movement
## 1316 Daphnia magna Branchiopoda Movement
## 1317 Daphnia magna Branchiopoda Movement
## 1318 Daphnia magna Branchiopoda Movement
## 1319 Daphnia magna Branchiopoda Movement
## 1320 Daphnia magna Branchiopoda Movement
## 1321 Daphnia magna Branchiopoda Movement
## 1322 Daphnia magna Branchiopoda Movement
## 1323 Daphnia magna Branchiopoda Movement
## 1324 Daphnia magna Branchiopoda Movement
## 1325 Daphnia magna Branchiopoda Movement
## 1326 Daphnia magna Branchiopoda Movement
## 1327 Daphnia magna Branchiopoda Movement
## 1328 Daphnia magna Branchiopoda Movement
## 1329 Xenopus laevis Amphibia Sociality
## 1330 Pimephales promelas Actinopterygii Foraging
## 1331 Gymnotus carapo Actinopterygii Movement
## 1332 Danio rerio Actinopterygii Boldness
## 1333 Betta splendens Actinopterygii Agression
## 1334 Danio rerio Actinopterygii Movement
## 1335 Danio rerio Actinopterygii Movement
## 1336 Betta splendens Actinopterygii Agression
## 1337 Danio rerio Actinopterygii Cognition
## 1338 Danio rerio Actinopterygii Cognition
## 1339 Pimephales promelas Actinopterygii Mating
## 1340 Danio rerio Actinopterygii Boldness
## 1341 Danio rerio Actinopterygii Boldness
## 1342 Danio rerio Actinopterygii Movement
## 1343 Danio rerio Actinopterygii Movement
## 1344 Danio rerio Actinopterygii Movement
## 1345 Danio rerio Actinopterygii Movement
## 1346 Danio rerio Actinopterygii Movement
## 1347 Danio rerio Actinopterygii Movement
## 1348 Danio rerio Actinopterygii Movement
## 1349 Danio rerio Actinopterygii Movement
## 1350 Danio rerio Actinopterygii Movement
## 1351 Danio rerio Actinopterygii Movement
## 1352 Lota lota Actinopterygii Movement
## 1353 Gambusia holbrooki Actinopterygii Mating
## 1354 Daphnia magna Branchiopoda Movement
## 1355 Daphnia magna Branchiopoda Movement
## 1356 Daphnia magna Branchiopoda Movement
## 1357 Daphnia magna Branchiopoda Movement
## 1358 Daphnia magna Branchiopoda Movement
## 1359 Gammarus pulex Malacostraca Mating
## 1360 Daphnia magna Branchiopoda Foraging
## 1361 Daphnia magna Branchiopoda Foraging
## 1362 Daphnia magna Branchiopoda Movement
## 1363 Artemia franciscana Branchiopoda Movement
## 1364 Daphnia magna Branchiopoda Movement
## 1365 Danio rerio Actinopterygii Boldness
## 1366 Danio rerio Actinopterygii Boldness
## 1367 Danio rerio Actinopterygii Boldness
## 1368 Danio rerio Actinopterygii Boldness
## 1369 Danio rerio Actinopterygii Boldness
## 1370 Hydra vulgaris Hydrozoa Foraging
## 1371 Hydra vulgaris Hydrozoa Foraging
## 1372 Hydra vulgaris Hydrozoa Foraging
## 1373 Hydra vulgaris Hydrozoa Foraging
## 1374 Hydra vulgaris Hydrozoa Foraging
## 1375 Hydra vulgaris Hydrozoa Foraging
## 1376 Hydra vulgaris Hydrozoa Foraging
## 1377 Hydra vulgaris Hydrozoa Foraging
## 1378 Hydra vulgaris Hydrozoa Foraging
## 1379 Hydra vulgaris Hydrozoa Foraging
## 1380 Hydra vulgaris Hydrozoa Foraging
## 1381 Danio rerio Actinopterygii Movement
## 1382 Danio rerio Actinopterygii Movement
## 1383 Danio rerio Actinopterygii Movement
## 1384 Danio rerio Actinopterygii Movement
## 1385 Danio rerio Actinopterygii Movement
## 1386 Danio rerio Actinopterygii Movement
## 1387 Danio rerio Actinopterygii Movement
## 1388 Danio rerio Actinopterygii Movement
## 1389 Danio rerio Actinopterygii Movement
## 1390 Danio rerio Actinopterygii Movement
## 1391 Danio rerio Actinopterygii Movement
## 1392 Danio rerio Actinopterygii Movement
## 1393 Danio rerio Actinopterygii Movement
## 1394 Danio rerio Actinopterygii Movement
## 1395 Danio rerio Actinopterygii Movement
## 1396 Danio rerio Actinopterygii Movement
## 1397 Danio rerio Actinopterygii Movement
## 1398 Danio rerio Actinopterygii Movement
## 1399 Danio rerio Actinopterygii Movement
## 1400 Danio rerio Actinopterygii Movement
## 1401 Danio rerio Actinopterygii Movement
## 1402 Danio rerio Actinopterygii Movement
## 1403 Danio rerio Actinopterygii Movement
## 1404 Danio rerio Actinopterygii Movement
## 1405 Danio rerio Actinopterygii Movement
## 1406 Danio rerio Actinopterygii Movement
## 1407 Danio rerio Actinopterygii Movement
## 1408 Danio rerio Actinopterygii Movement
## 1409 Danio rerio Actinopterygii Movement
## 1410 Danio rerio Actinopterygii Movement
## 1411 Danio rerio Actinopterygii Movement
## 1412 Danio rerio Actinopterygii Movement
## 1413 Danio rerio Actinopterygii Movement
## 1414 Danio rerio Actinopterygii Movement
## 1415 Danio rerio Actinopterygii Movement
## 1416 Danio rerio Actinopterygii Movement
## 1417 Danio rerio Actinopterygii Movement
## 1418 Danio rerio Actinopterygii Movement
## 1419 Danio rerio Actinopterygii Movement
## 1420 Danio rerio Actinopterygii Movement
## 1421 Danio rerio Actinopterygii Movement
## 1422 Danio rerio Actinopterygii Movement
## 1423 Danio rerio Actinopterygii Movement
## 1424 Danio rerio Actinopterygii Movement
## 1425 Danio rerio Actinopterygii Movement
## 1426 Danio rerio Actinopterygii Movement
## 1427 Danio rerio Actinopterygii Movement
## 1428 Danio rerio Actinopterygii Movement
## 1429 Danio rerio Actinopterygii Movement
## 1430 Danio rerio Actinopterygii Movement
## 1431 Danio rerio Actinopterygii Movement
## 1432 Danio rerio Actinopterygii Movement
## 1433 Gammarus pulex Malacostraca Movement
## 1434 Gammarus pulex Malacostraca Movement
## 1435 Gammarus pulex Malacostraca Movement
## 1436 Radix balthica Gastropoda Movement
## 1437 Gasterosteus aculeatus Actinopterygii Mating
## 1438 Pimephales promelas Actinopterygii Boldness
## 1439 Pelodiscus sinensis Reptilia Foraging
## 1440 Pelodiscus sinensis Reptilia Foraging
## 1441 Pelodiscus sinensis Reptilia Foraging
## 1442 Pelodiscus sinensis Reptilia Foraging
## 1443 Danio rerio Actinopterygii Movement
## 1444 Danio rerio Actinopterygii Movement
## 1445 Danio rerio Actinopterygii Boldness
## 1446 Danio rerio Actinopterygii Boldness
## 1447 Danio rerio Actinopterygii Movement
## 1448 Danio rerio Actinopterygii Movement
## 1449 Danio rerio Actinopterygii Boldness
## 1450 Danio rerio Actinopterygii Boldness
## 1451 Danio rerio Actinopterygii Boldness
## 1452 Danio rerio Actinopterygii Movement
## 1453 Danio rerio Actinopterygii Movement
## 1454 Danio rerio Actinopterygii Boldness
## 1455 Danio rerio Actinopterygii Boldness
## 1456 Danio rerio Actinopterygii Sociality
## 1457 Limnodynastes tasmaniensis Amphibia Foraging
## 1458 Danio rerio Actinopterygii Movement
## 1459 Danio rerio Actinopterygii Movement
## 1460 Procambarus clarkii Malacostraca Movement
## 1461 Procambarus clarkii Malacostraca Movement
## 1462 Pimephales promelas Actinopterygii Movement
## 1463 Physella acuta Gastropoda Movement
## 1464 Danio rerio Actinopterygii Boldness
## 1465 Danio rerio Actinopterygii Movement
## 1466 Pomacea canaliculata Gastropoda Mating
## 1467 Coenagrion hastulatum Insecta Foraging
## 1468 Coenagrion hastulatum Insecta Foraging
## 1469 Perca fluviatilis Actinopterygii Movement
## 1470 Danio rerio Actinopterygii Movement
## 1471 Danio rerio Actinopterygii Movement
## 1472 Danio rerio Actinopterygii Movement
## 1473 Danio rerio Actinopterygii Movement
## 1474 Mytilus edulis Bivalvia Foraging
## 1475 Mytilus galloprovincialis Bivalvia Foraging
## 1476 Danio rerio Actinopterygii Boldness
## 1477 Danio rerio Actinopterygii Boldness
## 1478 Danio rerio Actinopterygii Movement
## 1479 Mytilus galloprovincialis Bivalvia Not categorised
## 1480 Danio rerio Actinopterygii Not categorised
## 1481 Danio rerio Actinopterygii Not categorised
## 1482 Danio rerio Actinopterygii Not categorised
## 1483 Melanoides tuberculata Gastropoda Movement
## 1484 Carassius carassius Actinopterygii Foraging
## 1485 Danio rerio Actinopterygii Boldness
## 1486 Danio rerio Actinopterygii Movement
## 1487 Danio rerio Actinopterygii Movement
## 1488 Danio rerio Actinopterygii Movement
## 1489 Danio rerio Actinopterygii Movement
## 1490 Danio rerio Actinopterygii Boldness
## 1491 Oryzias latipes Actinopterygii Sociality
## 1492 Danio rerio Actinopterygii Movement
## 1493 Danio rerio Actinopterygii Boldness
## 1494 Danio rerio Actinopterygii Boldness
## 1495 Danio rerio Actinopterygii Movement
## 1496 Danio rerio Actinopterygii Movement
## 1497 Danio rerio Actinopterygii Movement
## 1498 Danio rerio Actinopterygii Boldness
## 1499 Danio rerio Actinopterygii Movement
## 1500 Danio rerio Actinopterygii Boldness
## 1501 Danio rerio Actinopterygii Boldness
## 1502 Danio rerio Actinopterygii Boldness
## 1503 Danio rerio Actinopterygii Boldness
## 1504 Danio rerio Actinopterygii Boldness
## 1505 Danio rerio Actinopterygii Boldness
## 1506 Danio rerio Actinopterygii Boldness
## 1507 Daphnia magna Branchiopoda Foraging
## 1508 Daphnia magna Branchiopoda Foraging
## 1509 Danio rerio Actinopterygii Boldness
## 1510 Danio rerio Actinopterygii Boldness
## 1511 Danio rerio Actinopterygii Boldness
## 1512 Danio rerio Actinopterygii Agression
## 1513 Danio rerio Actinopterygii Movement
## 1514 Daphnia magna Branchiopoda Foraging
## 1515 Danio rerio Actinopterygii Movement
## 1516 Daphnia magna Branchiopoda Foraging
## 1517 Daphnia magna Branchiopoda Foraging
## 1518 Xenopus laevis Amphibia Movement
## 1519 Danio rerio Actinopterygii Boldness
## 1520 Danio rerio Actinopterygii Boldness
## 1521 Danio rerio Actinopterygii Boldness
## 1522 Carassius auratus Actinopterygii Not categorised
## 1523 Danio rerio Actinopterygii Movement
## 1524 Danio rerio Actinopterygii Movement
## 1525 Danio rerio Actinopterygii Boldness
## 1526 Danio rerio Actinopterygii Mating
## 1527 Danio rerio Actinopterygii Movement
## 1528 Danio rerio Actinopterygii Movement
## 1529 Clarias gariepinus Actinopterygii Movement
## 1530 Salmo trutta Actinopterygii Movement
## 1531 Danio rerio Actinopterygii Movement
## 1532 Danio rerio Actinopterygii Movement
## 1533 Danio rerio Actinopterygii Movement
## 1534 Danio rerio Actinopterygii Movement
## 1535 Mytilus galloprovincialis Bivalvia Antipredator
## 1536 Corbicula fluminea Bivalvia Foraging
## 1537 Danio rerio Actinopterygii Movement
## 1538 Dugesia japonica Rhabditophora Movement
## 1539 Dugesia japonica Rhabditophora Movement
## 1540 Dugesia japonica Rhabditophora Movement
## 1541 Dugesia japonica Rhabditophora Movement
## 1542 Dugesia japonica Rhabditophora Movement
## 1543 Gobiocypris rarus Actinopterygii Movement
## 1544 Gobiocypris rarus Actinopterygii Movement
## 1545 Gobiocypris rarus Actinopterygii Movement
## 1546 Gobiocypris rarus Actinopterygii Movement
## 1547 Limnodynastes tasmaniensis Amphibia Movement
## 1548 Oryzias latipes Actinopterygii Boldness
## 1549 Danio rerio Actinopterygii Boldness
## 1550 Daphnia magna Branchiopoda Movement
## 1551 Danio rerio Actinopterygii Boldness
## 1552 Danio rerio Actinopterygii Boldness
## 1553 Cirrhinus mrigala Actinopterygii Foraging
## 1554 Daphnia magna Branchiopoda Movement
## 1555 Danio rerio Actinopterygii Movement
## 1556 Danio rerio Actinopterygii Movement
## 1557 Danio rerio Actinopterygii Movement
## 1558 Daphnia magna Branchiopoda Movement
## 1559 Danio rerio Actinopterygii Movement
## 1560 Danio rerio Actinopterygii Boldness
## 1561 Danio rerio Actinopterygii Movement
## 1562 Danio rerio Actinopterygii Movement
## 1563 Danio rerio Actinopterygii Movement
## 1564 Danio rerio Actinopterygii Boldness
## 1565 Daphnia magna Branchiopoda Movement
## 1566 Daphnia magna Branchiopoda Movement
## 1567 Danio rerio Actinopterygii Boldness
## 1568 Danio rerio Actinopterygii Mating
## 1569 Danio rerio Actinopterygii Boldness
## 1570 Rhamdia quelen Actinopterygii Movement
## 1571 Rhamdia quelen Actinopterygii Movement
## 1572 Danio rerio Actinopterygii Boldness
## 1573 Ilyanassa obsoleta Gastropoda Not categorised
## 1574 Ilyanassa obsoleta Gastropoda Not categorised
## 1575 Ilyanassa obsoleta Gastropoda Not categorised
## 1576 Ilyanassa obsoleta Gastropoda Not categorised
## 1577 Danio rerio Actinopterygii Not categorised
## 1578 Danio rerio Actinopterygii Not categorised
## 1579 Epinephelus fuscoguttatus Actinopterygii Foraging
## 1580 Daphnia magna Branchiopoda Foraging
## 1581 Danio rerio Actinopterygii Boldness
## 1582 Danio rerio Actinopterygii Movement
## 1583 Lithobates pipiens Amphibia Movement
## 1584 Salmo salar Actinopterygii Foraging
## 1585 Channa striata Actinopterygii Foraging
## 1586 Danio rerio Actinopterygii Movement
## 1587 Daphnia magna Branchiopoda Movement
## 1588 Danio rerio Actinopterygii Boldness
## 1589 Daphnia magna Branchiopoda Movement
## 1590 Daphnia magna Branchiopoda Movement
## 1591 Betta splendens Actinopterygii Mating
## 1592 Danio rerio Actinopterygii Movement
## 1593 Danio rerio Actinopterygii Movement
## 1594 Danio rerio Actinopterygii Movement
## 1595 Danio rerio Actinopterygii Mating
## 1596 Danio rerio Actinopterygii Mating
## 1597 Danio rerio Actinopterygii Movement
## 1598 Danio rerio Actinopterygii Movement
## 1599 Danio rerio Actinopterygii Movement
## 1600 Danio rerio Actinopterygii Movement
## 1601 Lymnaea stagnalis Gastropoda Movement
## 1602 Oreochromis niloticus Actinopterygii Movement
## 1603 Danio rerio Actinopterygii Movement
## 1604 Lymnaea stagnalis Gastropoda Movement
## 1605 Danio rerio Actinopterygii Movement
## 1606 Danio rerio Actinopterygii Movement
## 1607 Danio rerio Actinopterygii Movement
## 1608 Danio rerio Actinopterygii Movement
## 1609 Danio rerio Actinopterygii Movement
## 1610 Danio rerio Actinopterygii Boldness
## 1611 Daphnia magna Branchiopoda Movement
## 1612 Danio rerio Actinopterygii Movement
## 1613 Danio rerio Actinopterygii Boldness
## 1614 Danio rerio Actinopterygii Boldness
## 1615 Danio rerio Actinopterygii Boldness
## 1616 Danio rerio Actinopterygii Movement
## 1617 Danio rerio Actinopterygii Movement
## 1618 Daphnia pulex Branchiopoda Movement
## 1619 Danio rerio Actinopterygii Movement
## 1620 Daphnia pulex Branchiopoda Movement
## 1621 Gobio gobio Actinopterygii Boldness
## 1622 Neogobius fluviatilis Actinopterygii Boldness
## 1623 Perca fluviatilis Actinopterygii Movement
## 1624 Daphnia longispina Branchiopoda Movement
## 1625 Daphnia magna Branchiopoda Movement
## 1626 Gambusia affinis Actinopterygii Movement
## 1627 Oryzias sinensis Actinopterygii Movement
## 1628 Paramisgurnus dabryanus Actinopterygii Movement
## 1629 Rhodeus ocellatus Actinopterygii Movement
## 1630 Artemia salina Branchiopoda Movement
## 1631 Danio rerio Actinopterygii Movement
## 1632 Bufo bufo Amphibia Movement
## 1633 Rana dalmatina Amphibia Movement
## 1634 Bufo bufo Amphibia Movement
## 1635 Rana dalmatina Amphibia Movement
## 1636 Allogamus spp Insecta Foraging
## 1637 Chironomus riparius Insecta Foraging
## 1638 Allogamus spp Insecta Foraging
## 1639 Chironomus riparius Insecta Foraging
## 1640 Danio rerio Actinopterygii Movement
## 1641 Daphnia similis Branchiopoda Movement
## 1642 Danio rerio Actinopterygii Movement
## 1643 Daphnia similis Branchiopoda Movement
## 1644 Danio rerio Actinopterygii Movement
## 1645 Daphnia similis Branchiopoda Movement
## 1646 Danio rerio Actinopterygii Movement
## 1647 Daphnia similis Branchiopoda Movement
## 1648 Culex quinquefasciatus Insecta Foraging
## 1649 Gambusia holbrooki Actinopterygii Foraging
## 1650 Carcinus maenas Malacostraca Not categorised
## 1651 Sepia officinalis Cephalopoda Not categorised
## 1652 Danio rerio Actinopterygii Movement
## 1653 Danio rerio Actinopterygii Movement
## 1654 Danio rerio Actinopterygii Movement
## 1655 Ilyanassa obsoleta Gastropoda Not categorised
## 1656 Leptoxis carinata Gastropoda Not categorised
## 1657 Ilyanassa obsoleta Gastropoda Not categorised
## 1658 Leptoxis carinata Gastropoda Not categorised
## 1659 Ilyanassa obsoleta Gastropoda Not categorised
## 1660 Leptoxis carinata Gastropoda Not categorised
## 1661 Physalaemus albonotatus Amphibia Movement
## 1662 Trachycephalus typhonius Amphibia Movement
## 1663 Gibbula unbilicalis Gastropoda Not categorised
## 1664 Lymnaea stagnalis Gastropoda Not categorised
## 1665 Danio rerio Actinopterygii Movement
## 1666 Pimephales promelas Actinopterygii Movement
## 1667 Danio rerio Actinopterygii Movement
## 1668 Pimephales promelas Actinopterygii Movement
## 1669 Danio rerio Actinopterygii Movement
## 1670 Pimephales promelas Actinopterygii Movement
## 1671 Amphibalanus amphitrite Thecostraca Movement
## 1672 Brachionus plicatilis Monogononta Movement
## 1673 Lithopoma americanum Gastropoda Movement
## 1674 Urosalpinx cinerea Gastropoda Movement
## 1675 Lithopoma americanum Gastropoda Movement
## 1676 Urosalpinx cinerea Gastropoda Movement
## 1677 Coenagrion hastulatum Insecta Movement
## 1678 Perca fluviatilis Actinopterygii Movement
## 1679 Nerophis lumbriciformis Actinopterygii Mating
## 1680 Syngnathus abaster Actinopterygii Mating
## 1681 Chlorostoma funebralis Gastropoda Movement
## 1682 Lithopoma americanum Gastropoda Movement
## 1683 Nucella ostrina Gastropoda Movement
## 1684 Tegula fasciatus Gastropoda Movement
## 1685 Urosalpinx cinerea Gastropoda Movement
## 1686 Chlorostoma funebralis Gastropoda Movement
## 1687 Lithopoma americanum Gastropoda Movement
## 1688 Nucella ostrina Gastropoda Movement
## 1689 Tegula fasciatus Gastropoda Movement
## 1690 Urosalpinx cinerea Gastropoda Movement
## 1691 Chlorostoma funebralis Gastropoda Movement
## 1692 Lithopoma americanum Gastropoda Movement
## 1693 Nucella ostrina Gastropoda Movement
## 1694 Tegula fasciatus Gastropoda Movement
## 1695 Urosalpinx cinerea Gastropoda Movement
## 1696 Chlorostoma funebralis Gastropoda Movement
## 1697 Lithopoma americanum Gastropoda Movement
## 1698 Nucella ostrina Gastropoda Movement
## 1699 Tegula fasciatus Gastropoda Movement
## 1700 Urosalpinx cinerea Gastropoda Movement
## 1701 Carassius auratus Actinopterygii Boldness
## 1702 Danio rerio Actinopterygii Movement
## 1703 Carassius auratus Actinopterygii Boldness
## 1704 Leptoxis carinata Gastropoda Movement
## 1705 Stagnicola elodes Gastropoda Movement
## 1706 Leptoxis carinata Gastropoda Movement
## 1707 Stagnicola elodes Gastropoda Movement
## 1708 Oncorhynchus mykiss Actinopterygii Agression
## 1709 Poecilia reticulata Actinopterygii Mating
## 1710 Lampsilis cardium Bivalvia Mating
## 1711 Lampsilis fasciola Bivalvia Mating
## 1712 Asterias rubens Asteroidea Foraging
## 1713 Amatitlania nigrofasciata Actinopterygii Movement
## 1714 Poecilia sphenops Actinopterygii Movement
## 1715 Morone saxatilis x morone chrysops Actinopterygii Foraging
## 1716 Daphnia magna Branchiopoda Movement
## 1717 Daphnia magna Branchiopoda Movement
## 1718 Daphnia magna Branchiopoda Movement
## 1719 Pimephales promelas Actinopterygii Movement
## 1720 Pimephales promelas Actinopterygii Movement
## 1721 Pimephales promelas Actinopterygii Movement
## 1722 Crangonyx shoemakeri Malacostraca Movement
## 1723 Gammarus minus Malacostraca Movement
## 1724 Stygobromus tenuis Malacostraca Movement
## 1725 Daphnia magna Branchiopoda Movement
## 1726 Moina macrocopa Branchiopoda Movement
## 1727 Lithobates pipiens Amphibia Movement
## 1728 Lithobates pipiens Amphibia Movement
## 1729 Lithobates pipiens Amphibia Movement
## 1730 Lithobates pipiens Amphibia Movement
## 1731 Cyprinus carpio Actinopterygii Movement
## 1732 Danio rerio Actinopterygii Boldness
## 1733 Danio rerio Actinopterygii Boldness
## 1734 Lymnaea stagnalis Gastropoda Movement
## 1735 Danio rerio Actinopterygii Movement
## 1736 Daphnia magna Branchiopoda Foraging
## 1737 Daphnia magna Branchiopoda Foraging
## 1738 Daphnia magna Branchiopoda Foraging
## 1739 Cyprinus carpio Actinopterygii Foraging
## 1740 Cyprinus carpio Actinopterygii ForagingLet’s look at the 10 most common classes and ATCs
PICO_class_atc <- PICO_df %>%
dplyr::filter(!is.na(compound_atc_level_3), !is.na(species_class)) %>%
tidyr::separate_rows(compound_atc_level_3, sep = ";") %>%
dplyr::mutate(compound_atc_level_3 = str_trim(compound_atc_level_3))
PICO_atc_10 <- PICO_class_atc %>%
dplyr::group_by(compound_atc_level_3) %>%
dplyr::reframe(n = n()) %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:10) %>%
dplyr::pull(compound_atc_level_3)
PICO_class_10 <- PICO_class_atc %>%
dplyr::group_by(species_class) %>%
dplyr::reframe(n = n()) %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:10) %>%
dplyr::pull(species_class)
PICO_class_10 <- PICO_class_atc %>%
dplyr::group_by(species_class) %>%
dplyr::reframe(n = n()) %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:10) %>%
dplyr::pull(species_class)
behav_cat_order <- PICO_class_atc %>%
dplyr::filter(compound_atc_level_3 %in% PICO_atc_10 & species_class %in% PICO_class_10) %>%
dplyr::group_by(behav_cat) %>%
dplyr::reframe(n = n()) %>%
dplyr::arrange(desc(n)) %>%
dplyr::pull(behav_cat)
PICO_class_atc_10 <- PICO_class_atc %>%
dplyr::filter(compound_atc_level_3 %in% PICO_atc_10 & species_class %in% PICO_class_10) %>%
dplyr::mutate(compound_atc_level_3 = factor(compound_atc_level_3, levels = PICO_atc_10),
species_class = factor(species_class, levels = PICO_class_10),
behav_cat = factor(behav_cat, levels = behav_cat_order)) %>%
dplyr::select(compound_atc_level_3, behav_cat, species_class)PICO_atc_class_sankey <- highcharter::hchart(data_to_sankey(PICO_class_atc_10), "sankey")
PICO_atc_class_sankeysetwd(figures_path)
htmlwidgets::saveWidget(widget = PICO_atc_class_sankey, file = "PICO_atc_class_sankey.html")setwd(figures_path)
# Make a webshot in pdf : high quality but can not choose printed zone
webshot::webshot("PICO_atc_class_sankey.html" , "PICO_atc_class_sankey.pdf", delay = 10)16.1 Common compounds
Lets take a closer look at the 3 most common compounds
EIPAAB_database %>%
dplyr::group_by(compound_name) %>%
dplyr::reframe(n = n()) %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:3)## # A tibble: 3 × 2
## compound_name n
## <chr> <int>
## 1 Fluoxetine 201
## 2 Diazepam 67
## 3 17-alpha-ethinylestradiol 6316.1.1 Fluoxetine
spp_order <- PICO_df %>%
dplyr::filter(compound_name == "Fluoxetine") %>%
dplyr::group_by(species_name) %>%
dplyr::reframe(n = n()) %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:5) %>%
pull(species_name)
beh_order <- PICO_df %>%
dplyr::filter(compound_name == "Fluoxetine" & species_name %in% spp_order) %>%
dplyr::group_by(behav_cat) %>%
dplyr::reframe(n = n()) %>%
dplyr::arrange(desc(n)) %>%
pull(behav_cat)
PICO_fluoxetine <- PICO_df %>%
dplyr::filter(compound_name == "Fluoxetine" & species_name %in% spp_order) %>%
dplyr::mutate(
species_name = factor(species_name, levels = spp_order),
behav_cat = factor(behav_cat, levels = beh_order),
) %>%
dplyr::select(species_name, behav_cat)PICO_fluoxetine %>%
dplyr::filter(species_name == "Betta splendens") %>%
dplyr::distinct(behav_cat)## behav_cat
## 1 Boldness
## 2 Agression
## 3 Mating
## 4 MovementPICO_fluoxetine_sankey <- highcharter::hchart(data_to_sankey(PICO_fluoxetine), "sankey", name = "PICO")
PICO_fluoxetine_sankeysetwd(figures_path)
htmlwidgets::saveWidget(widget = PICO_fluoxetine_sankey, file = "PICO_fluoxetine_sankey.html")setwd(figures_path)
# Make a webshot in pdf : high quality but can not choose printed zone
webshot::webshot("PICO_fluoxetine_sankey.html" , "PICO_fluoxetine_sankey.pdf", delay = 10)16.1.2 Diazepam
spp_order <- PICO_df %>%
dplyr::filter(compound_name == "Diazepam") %>%
dplyr::group_by(species_name) %>%
dplyr::reframe(n = n()) %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:5) %>%
pull(species_name)
beh_order <- PICO_df %>%
dplyr::filter(compound_name == "Diazepam" & species_name %in% spp_order) %>%
dplyr::group_by(behav_cat) %>%
dplyr::reframe(n = n()) %>%
dplyr::arrange(desc(n)) %>%
pull(behav_cat)
PICO_diazepam <- PICO_df %>%
dplyr::filter(compound_name == "Diazepam" & species_name %in% spp_order) %>%
dplyr::mutate(
species_name = factor(species_name, levels = spp_order),
behav_cat = factor(behav_cat, levels = beh_order),
) %>%
dplyr::select(species_name, behav_cat)PICO_diazepam_sankey <- highcharter::hchart(data_to_sankey(PICO_diazepam), "sankey", name = "PICO")
PICO_diazepam_sankeysetwd(figures_path)
htmlwidgets::saveWidget(widget = PICO_diazepam_sankey, file = "PICO_diazepam_sankey.html")setwd(figures_path)
# Make a webshot in pdf : high quality but can not choose printed zone
webshot::webshot("PICO_diazepam_sankey.html" , "PICO_diazepam_sankey.pdf", delay = 10)16.3.1 17-alpha-ethinylestradiol
spp_order <- PICO_df %>%
dplyr::filter(compound_name == "17-alpha-ethinylestradiol") %>%
dplyr::group_by(species_name) %>%
dplyr::reframe(n = n()) %>%
dplyr::arrange(desc(n)) %>%
dplyr::slice(1:5) %>%
pull(species_name)
beh_order <- PICO_df %>%
dplyr::filter(compound_name == "17-alpha-ethinylestradiol" & species_name %in% spp_order) %>%
dplyr::group_by(behav_cat) %>%
dplyr::reframe(n = n()) %>%
dplyr::arrange(desc(n)) %>%
pull(behav_cat)
PICO_EE2 <- PICO_df %>%
dplyr::filter(compound_name == "17-alpha-ethinylestradiol" & species_name %in% spp_order) %>%
dplyr::mutate(
species_name = factor(species_name, levels = spp_order),
behav_cat = factor(behav_cat, levels = beh_order),
) %>%
dplyr::select(species_name, behav_cat)PICO_EE2_sankey <- highcharter::hchart(data_to_sankey(PICO_EE2), "sankey", name = "PICO")
PICO_EE2_sankeysetwd(figures_path)
htmlwidgets::saveWidget(widget = PICO_EE2_sankey, file = "PICO_EE2_sankey.html")setwd(figures_path)
# Make a webshot in pdf : high quality but can not choose printed zone
webshot::webshot("PICO_EE2_sankey.html" , "PICO_EE2_sankey.pdf", delay = 10)17 Knowledge clusters and gaps
Identify knowledge clusters and gaps.
We will also do this by study motivation, because the knowleage gaps will be motivation spesfic.
17.1 Species class
First look by species class
Making a dataframe
behav_cat_class_long <- PICO_df %>%
dplyr::group_by(study_motivation, species_class, behav_cat) %>%
dplyr::reframe(count = n()) %>%
tidyr::complete(study_motivation, species_class, behav_cat, fill = list(count = 0)) %>%
dplyr::group_by(study_motivation, species_class) %>%
dplyr::mutate(total_class_motivation = sum(count)) %>%
dplyr::ungroup() %>%
dplyr::mutate(rel_percent = round(count/total_class_motivation*100,0),
rel_percent = if_else(is.finite(rel_percent), rel_percent, 0)
)
class_order <- behav_cat_class_long %>%
dplyr::group_by(species_class) %>%
dplyr::reframe(n = sum(count)) %>%
dplyr::arrange(n) %>%
dplyr::pull(species_class)
behav_cat_order <- behav_cat_class_long %>%
dplyr::group_by(behav_cat) %>%
dplyr::reframe(n = sum(count)) %>%
dplyr::arrange(desc(n)) %>%
dplyr::pull(behav_cat)
behav_cat_class_long <- behav_cat_class_long %>%
dplyr::mutate(species_class = factor(species_class, levels = class_order),
behav_cat = factor(behav_cat, levels = behav_cat_order)
)cust_col <- colorRampPalette(c("#FDEDF4", "#F068A7"))(30)
behav_class_hm <- behav_cat_class_long %>%
ggplot(aes(x = behav_cat, y = species_class, fill = count)) +
geom_tile() +
#geom_text(aes(label = ifelse(count == 0, NA, count)), color = "black", size = 3) +
scale_fill_gradientn(colors = cust_col, na.value = "white", limits = c(1, max(behav_cat_class_long$count, na.rm = TRUE)), guide = "none") +
theme_bw() +
facet_wrap(~study_motivation) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(
x = "Behaviour",
y = "Species Class",
fill = "Count"
)
behav_class_hm
setwd(figures_path)
ggsave("behav_class_hm.pdf", plot = behav_class_hm, width = 8.3, height = 11.7/2)This one uses relative vaules for each class (e.g. row in the heat map)
cust_col <- colorRampPalette(brewer.pal(4, "Oranges"))(30)
behav_class_rel_hm <- behav_cat_class_long %>%
ggplot(aes(x = behav_cat, y = species_class, fill = rel_percent)) +
geom_tile() +
#geom_text(aes(label = ifelse(rel_percent == 0, NA, rel_percent)), color = "black", size = 3) +
scale_fill_gradientn(colors = cust_col, na.value = "white", limits = c(1, max(behav_cat_class_long$count, na.rm = TRUE)), guide = "none") +
theme_bw() +
facet_wrap(~study_motivation) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(
x = "Behaviour",
y = "Species Class",
fill = "Count"
)
behav_class_rel_hm
setwd(figures_path)
ggsave("behav_class_rel_hm.pdf", plot = behav_class_rel_hm, width = 8.3, height = 11.7/2)17.2 Compound cluster/gaps
Now looking by compound
Making a dataframe
behav_atc_long <- PICO_df %>%
separate_rows(compound_atc_level_3, sep = ";") %>%
dplyr::group_by(study_motivation, compound_atc_level_3, behav_cat) %>%
dplyr::reframe(count = n()) %>%
tidyr::complete(study_motivation, compound_atc_level_3, behav_cat, fill = list(count = 0)) %>%
dplyr::group_by(study_motivation, compound_atc_level_3) %>%
dplyr::mutate(total_atc_motivation = sum(count)) %>%
dplyr::ungroup() %>%
dplyr::mutate(rel_percent = round(count/total_atc_motivation*100,0),
rel_percent = if_else(is.finite(rel_percent), rel_percent, 0)
)
atc_order <- behav_atc_long %>%
dplyr::group_by(compound_atc_level_3) %>%
dplyr::reframe(n = sum(count)) %>%
dplyr::arrange(n) %>%
dplyr::pull(compound_atc_level_3)
behav_cat_order <- behav_atc_long %>%
dplyr::group_by(behav_cat) %>%
dplyr::reframe(n = sum(count)) %>%
dplyr::arrange(desc(n)) %>%
dplyr::pull(behav_cat)
behav_atc_long <- behav_atc_long %>%
dplyr::mutate(compound_atc_level_3 = factor(compound_atc_level_3, levels = atc_order),
behav_cat = factor(behav_cat, levels = behav_cat_order)
)behav_atc_long %>%
dplyr::distinct(compound_atc_level_3)## # A tibble: 132 × 1
## compound_atc_level_3
## <fct>
## 1 a01a stomatological preparations
## 2 a02b drugs for peptic ulcer and gastro-oesophageal reflux disease (gord)
## 3 a03b belladonna and derivatives, plain
## 4 a03f propulsives
## 5 a04a antiemetics and antinauseants
## 6 a06a drugs for constipation
## 7 a07a intestinal antiinfectives
## 8 a07e intestinal antiinflammatory agents
## 9 a08a antiobesity preparations, excl. diet products
## 10 a10b blood glucose lowering drugs, excl. insulins
## # ℹ 122 more rowscust_col <- colorRampPalette(c("#FDEDF4", "#F068A7"))(30)
behav_atc_hm <- behav_atc_long %>%
ggplot(aes(x = behav_cat, y = compound_atc_level_3, fill = count)) +
geom_tile() +
#geom_text(aes(label = ifelse(count == 0, NA, count)), color = "black", size = 3) +
scale_fill_gradientn(colors = cust_col, na.value = "white", limits = c(1, max(behav_atc_long$count, na.rm = TRUE)), guide = "none") +
theme_void() +
facet_wrap(~study_motivation) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(
x = "Behaviour",
y = "Species Class",
fill = "Count"
)
behav_atc_hm
setwd(figures_path)
ggsave("behav_class_hm.pdf", plot = behav_class_hm, width = 8.3, height = 11.7/2)This one uses relative vaules for each class (e.g. row in the heat map)
cust_col <- colorRampPalette(brewer.pal(4, "Oranges"))(30)
behav_class__rel_hm <- behav_cat_class_long %>%
ggplot(aes(x = behav_cat, y = species_class, fill = rel_percent)) +
geom_tile() +
#geom_text(aes(label = ifelse(rel_percent == 0, NA, rel_percent)), color = "black", size = 3) +
scale_fill_gradientn(colors = cust_col, na.value = "white", limits = c(1, max(behav_cat_class_long$count, na.rm = TRUE)), guide = "none") +
theme_bw() +
facet_wrap(~study_motivation) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(
x = "Behaviour",
y = "Species Class",
fill = "Count"
)
behav_class__rel_hm
setwd(figures_path)
ggsave("behav_class__rel_hm.pdf", plot = behav_class__rel_hm, width = 8.3, height = 11.7/2)18 Additional biomarkers
EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::group_by(additional_biomarkers) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(total = sum(n),
percent = n/total)## # A tibble: 2 × 4
## additional_biomarkers n total percent
## <chr> <int> <int> <dbl>
## 1 No 435 901 0.483
## 2 Yes 466 901 0.517EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::group_by(validity_survival_growth_reproduction) %>%
dplyr::reframe(n = n()) %>%
dplyr::mutate(total = sum(n),
percent = n/total)## # A tibble: 2 × 4
## validity_survival_growth_reproduction n total percent
## <chr> <int> <int> <dbl>
## 1 No 543 901 0.603
## 2 Yes 358 901 0.39719 Validity
This a list of all 19 metadata that relate to our validity information.
c(“validity_guideline”, “validity_good_laboratory_practice”, “validity_survival_growth_reproduction”, “validity_animal_feeding”, “validity_water_quality”, “validity_light_cycle”, “validity_randomization”, “validity_behav_scoring_method”, “validity_behav_blinding”, “validity_conflict_statement”, “species_source”, “species_stage”, “species_sex”, “compound_min_duration_exposure”, “compound_max_duration_exposure”, “validity_compound_cas_reported”, “validity_compound_purity_reported”, “validity_compound_water_verification”, “validity_compound_animal_verification”)
19.1 Did it follow a guideline (CRED Q1)
guideline <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(validity_guideline, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
guideline_all <- guideline %>%
dplyr::group_by(validity_guideline) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
guideline_all <- rbind(guideline_all, guideline) %>%
dplyr::filter(validity_guideline == "Yes")
guideline_all## # A tibble: 4 × 4
## validity_guideline n study_motivation percent
## <chr> <int> <chr> <dbl>
## 1 Yes 135 Overall 15
## 2 Yes 111 Environmental 21.8
## 3 Yes 14 Medical 6
## 4 Yes 10 Basic research 6.319.2 Did it use good laboratory practice (CRED Q2)
GLP <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(validity_good_laboratory_practice, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
GLP_all <- GLP %>%
dplyr::group_by(validity_good_laboratory_practice) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
GLP_all <- rbind(GLP_all, GLP) %>%
dplyr::filter(validity_good_laboratory_practice == "Yes")
GLP_all## # A tibble: 3 × 4
## validity_good_laboratory_practice n study_motivation percent
## <chr> <int> <chr> <dbl>
## 1 Yes 6 Overall 0.7
## 2 Yes 5 Environmental 1
## 3 Yes 1 Medical 0.419.3 Did it report survival, growth and/or reproduction (CRED Q3)
survival_growth_reproduction <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(validity_survival_growth_reproduction, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
survival_growth_reproduction_all <- survival_growth_reproduction %>%
dplyr::group_by(validity_survival_growth_reproduction) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
survival_growth_reproduction_all <- rbind(survival_growth_reproduction_all, survival_growth_reproduction) %>%
dplyr::filter(validity_survival_growth_reproduction == "Yes")
survival_growth_reproduction_all## # A tibble: 4 × 4
## validity_survival_growth_reproduction n study_motivation percent
## <chr> <int> <chr> <dbl>
## 1 Yes 358 Overall 39.7
## 2 Yes 273 Environmental 53.5
## 3 Yes 61 Medical 26.1
## 4 Yes 24 Basic research 15.319.4 Did it report compound cas (CRED Q5)
CAS <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(validity_compound_cas_reported, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
CAS_all <- CAS %>%
dplyr::group_by(validity_compound_cas_reported) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
CAS_all <- rbind(CAS_all, CAS) %>%
dplyr::filter(validity_compound_cas_reported == "Yes")
CAS_all## # A tibble: 4 × 4
## validity_compound_cas_reported n study_motivation percent
## <chr> <int> <chr> <dbl>
## 1 Yes 223 Overall 24.8
## 2 Yes 187 Environmental 36.7
## 3 Yes 25 Medical 10.7
## 4 Yes 11 Basic research 719.5 Did it report compound purity (CRED Q6)
purity <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(validity_compound_purity_reported, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
purity_all <- purity %>%
dplyr::group_by(validity_compound_purity_reported) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
purity_all <- rbind(purity_all, purity) %>%
dplyr::filter(validity_compound_purity_reported == "Yes")
purity_all## # A tibble: 4 × 4
## validity_compound_purity_reported n study_motivation percent
## <chr> <int> <chr> <dbl>
## 1 Yes 228 Overall 25.3
## 2 Yes 198 Environmental 38.8
## 3 Yes 21 Medical 9
## 4 Yes 9 Basic research 5.719.6 Species infomartion (CRED Q8)
19.6.1 Life stage
stage <- EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(species_stage, study_motivation) %>%
dplyr::reframe(n = n()) %>%
tidyr::separate_rows(species_stage, sep = ";") %>%
dplyr::group_by(species_stage, study_motivation) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
stage_all <- stage %>%
dplyr::group_by(species_stage) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
stage_all <- rbind(stage_all, stage) %>%
dplyr::filter(species_stage == "Unknown or not specified") %>%
dplyr::mutate(percent_reported = 100-percent)
stage_all## # A tibble: 4 × 5
## species_stage n study_motivation percent percent_reported
## <chr> <int> <chr> <dbl> <dbl>
## 1 Unknown or not specified 166 Overall 16.6 83.4
## 2 Unknown or not specified 100 Environmental 17.1 82.9
## 3 Unknown or not specified 22 Medical 9 91
## 4 Unknown or not specified 44 Basic research 26.5 73.519.6.1 Sex
sex <- EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(species_sex, study_motivation) %>%
dplyr::reframe(n = n()) %>%
tidyr::separate_rows(species_sex, sep = ";") %>%
dplyr::group_by(species_sex, study_motivation) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
sex_all <- sex %>%
dplyr::group_by(species_sex) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
sex_all <- rbind(sex_all, sex) %>%
dplyr::filter(species_sex == "Unknown or not specified") %>%
dplyr::mutate(percent_reported = 100-percent)
sex_all## # A tibble: 4 × 5
## species_sex n study_motivation percent percent_reported
## <chr> <int> <chr> <dbl> <dbl>
## 1 Unknown or not specified 546 Overall 46.5 53.5
## 2 Unknown or not specified 325 Environmental 50.4 49.6
## 3 Unknown or not specified 132 Medical 41 59
## 4 Unknown or not specified 89 Basic research 43.2 56.819.7 The soruce of species (CRED Q9)
source <- EIPAAB_database %>%
dplyr::group_by(unique_population_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(species_source, study_motivation) %>%
dplyr::reframe(n = n()) %>%
tidyr::separate_rows(species_source, sep = ";") %>%
dplyr::group_by(species_source, study_motivation) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
source_all <- source %>%
dplyr::group_by(species_source) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
source_all <- rbind(source_all, source) %>%
dplyr::filter(species_source == "Not reported") %>%
dplyr::mutate(percent_reported = 100-percent)
source_all## # A tibble: 4 × 5
## species_source n study_motivation percent percent_reported
## <chr> <int> <chr> <dbl> <dbl>
## 1 Not reported 148 Overall 15.6 84.4
## 2 Not reported 72 Environmental 13.1 86.9
## 3 Not reported 51 Medical 21.2 78.8
## 4 Not reported 25 Basic research 15.5 84.519.8 experimental system (CRED Q11)
19.8.1 Feeding
feeding <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(validity_animal_feeding, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
feeding_all <- feeding %>%
dplyr::group_by(validity_animal_feeding) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
feeding_all <- rbind(feeding_all, feeding) %>%
dplyr::filter(validity_animal_feeding == "Yes")
feeding_all## # A tibble: 4 × 4
## validity_animal_feeding n study_motivation percent
## <chr> <int> <chr> <dbl>
## 1 Yes 716 Overall 79.5
## 2 Yes 430 Environmental 84.3
## 3 Yes 159 Medical 68.2
## 4 Yes 127 Basic research 80.419.8.1 Water quality
water <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(validity_water_quality, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
water_all <- water %>%
dplyr::group_by(validity_water_quality) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
water_all <- rbind(water_all, water) %>%
dplyr::filter(validity_water_quality == "Yes")
water_all## # A tibble: 4 × 4
## validity_water_quality n study_motivation percent
## <chr> <int> <chr> <dbl>
## 1 Yes 806 Overall 89.5
## 2 Yes 473 Environmental 92.7
## 3 Yes 206 Medical 88.4
## 4 Yes 127 Basic research 80.419.8.1 light cycle
light <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(validity_light_cycle, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
light_all <- light %>%
dplyr::group_by(validity_light_cycle) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
light_all <- rbind(light_all, light) %>%
dplyr::filter(validity_light_cycle == "Yes")
light_all## # A tibble: 4 × 4
## validity_light_cycle n study_motivation percent
## <chr> <int> <chr> <dbl>
## 1 Yes 756 Overall 83.9
## 2 Yes 429 Environmental 84.1
## 3 Yes 200 Medical 85.8
## 4 Yes 127 Basic research 80.419.9 Exposure duration (CRED Q14)
19.9.1 Minimum duration
min_duration <- EIPAAB_database %>%
dplyr::group_by(compound_min_duration_exposure, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
min_duration_all <- min_duration %>%
dplyr::group_by(compound_min_duration_exposure) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
min_duration_all <- rbind(min_duration_all, min_duration) %>%
dplyr::filter(compound_min_duration_exposure == "Not stated") %>%
dplyr::mutate(percent_reported = 100-percent)
min_duration_all## # A tibble: 4 × 5
## compound_min_duration_exposure n study_motivation percent percent_reported
## <chr> <int> <chr> <dbl> <dbl>
## 1 Not stated 102 Overall 5.9 94.1
## 2 Not stated 38 Environmental 4.4 95.6
## 3 Not stated 52 Medical 10 90
## 4 Not stated 12 Basic research 3.3 96.719.9.2 Maximum duration
max_duration <- EIPAAB_database %>%
dplyr::group_by(compound_max_duration_exposure, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
max_duration_all <- max_duration %>%
dplyr::group_by(compound_max_duration_exposure) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
max_duration_all <- rbind(max_duration_all, max_duration) %>%
dplyr::filter(compound_max_duration_exposure == "Not stated") %>%
dplyr::mutate(percent_reported = 100-percent)
max_duration_all## # A tibble: 4 × 5
## compound_max_duration_exposure n study_motivation percent percent_reported
## <chr> <int> <chr> <dbl> <dbl>
## 1 Not stated 96 Overall 5.5 94.5
## 2 Not stated 32 Environmental 3.7 96.3
## 3 Not stated 53 Medical 10.2 89.8
## 4 Not stated 11 Basic research 3 9719.10 Exposure verification (CRED Q15)
19.10.1 Water verification
water_verification <- EIPAAB_database %>%
dplyr::filter(!is.na(validity_compound_water_verification)) %>%
dplyr::group_by(validity_compound_water_verification, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
water_verification_all <- water_verification %>%
dplyr::group_by(validity_compound_water_verification) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
water_verification_all <- rbind(water_verification_all, water_verification) %>%
dplyr::filter(validity_compound_water_verification == "Measured")
water_verification_all## # A tibble: 4 × 4
## validity_compound_water_verification n study_motivation percent
## <chr> <int> <chr> <dbl>
## 1 Measured 313 Overall 20.6
## 2 Measured 295 Environmental 35.8
## 3 Measured 10 Medical 2.5
## 4 Measured 8 Basic research 2.719.10.1 Tissue verification
tissue_verification <- EIPAAB_database %>%
#dplyr::filter(!is.na(validity_compound_animal_verification)) %>%
dplyr::group_by(validity_compound_animal_verification, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
tissue_verification_all <- tissue_verification %>%
dplyr::group_by(validity_compound_animal_verification) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
tissue_verification_all <- rbind(tissue_verification_all, tissue_verification) %>%
dplyr::filter(validity_compound_animal_verification == "Yes")
tissue_verification_all## # A tibble: 4 × 4
## validity_compound_animal_verification n study_motivation percent
## <chr> <int> <chr> <dbl>
## 1 Yes 154 Overall 8.9
## 2 Yes 115 Environmental 13.4
## 3 Yes 22 Medical 4.2
## 4 Yes 17 Basic research 4.719.11 Randomization (Not CRED)
randomization <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(validity_randomization, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
randomization_all <- randomization %>%
dplyr::group_by(validity_randomization) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
randomization_all <- rbind(randomization_all, randomization) %>%
dplyr::filter(validity_randomization == "Yes") %>%
dplyr::mutate(percent_disclosed = 100-percent)
randomization_all## # A tibble: 4 × 5
## validity_randomization n study_motivation percent percent_disclosed
## <chr> <int> <chr> <dbl> <dbl>
## 1 Yes 362 Overall 40.2 59.8
## 2 Yes 229 Environmental 44.9 55.1
## 3 Yes 75 Medical 32.2 67.8
## 4 Yes 58 Basic research 36.7 63.319.12 Blinding (Not CRED)
blinding <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(validity_behav_blinding, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
blinding_all <- blinding %>%
dplyr::group_by(validity_behav_blinding) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
blinding_all <- rbind(blinding_all, blinding) %>%
dplyr::filter(validity_behav_blinding == "Yes")
blinding_all## # A tibble: 4 × 4
## validity_behav_blinding n study_motivation percent
## <chr> <int> <chr> <dbl>
## 1 Yes 153 Overall 17
## 2 Yes 75 Environmental 14.7
## 3 Yes 44 Medical 18.9
## 4 Yes 34 Basic research 21.519.13 Scoring method (Not CRED)
behav_scoring <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
tidyr::separate_rows(validity_behav_scoring_method, sep = ";") %>%
dplyr::group_by(validity_behav_scoring_method, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
behav_scoring_all <- behav_scoring %>%
dplyr::group_by(validity_behav_scoring_method) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
behav_scoring_all <- rbind(behav_scoring_all, behav_scoring) %>%
dplyr::filter(validity_behav_scoring_method == "not specified") %>%
dplyr::mutate(percent_specified = 100-percent)
behav_scoring_all## # A tibble: 4 × 5
## validity_behav_scoring_method n study_motivation percent percent_specified
## <chr> <int> <chr> <dbl> <dbl>
## 1 not specified 221 Overall 22.7 77.3
## 2 not specified 130 Environmental 24 76
## 3 not specified 56 Medical 21.5 78.5
## 4 not specified 35 Basic research 20.6 79.419.14 Conflict statement (Not CRED)
conflict <- EIPAAB_database %>%
dplyr::group_by(article_id) %>%
dplyr::sample_n(1) %>%
dplyr::ungroup() %>%
dplyr::group_by(validity_conflict_statement, study_motivation) %>%
dplyr::reframe(n = n()) %>%
dplyr::group_by(study_motivation) %>%
dplyr::mutate(total_motivation = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(percent = round(n/total_motivation*100,1)) %>%
dplyr::select(-total_motivation)
conflict_all <- conflict %>%
dplyr::group_by(validity_conflict_statement) %>%
dplyr::reframe(n = sum(n)) %>%
dplyr::mutate(study_motivation = "Overall",
percent = round(n/sum(n)*100,1))
conflict_all <- rbind(conflict_all, conflict) %>%
dplyr::filter(validity_conflict_statement == "No statement is made in the paper") %>%
dplyr::mutate(percent_specified = 100-percent)
conflict_all## # A tibble: 4 × 5
## validity_conflict_statement n study_motivation percent percent_specified
## <chr> <int> <chr> <dbl> <dbl>
## 1 No statement is made in the … 407 Overall 45.2 54.8
## 2 No statement is made in the … 254 Environmental 49.8 50.2
## 3 No statement is made in the … 65 Medical 27.9 72.1
## 4 No statement is made in the … 88 Basic research 55.7 44.3
15.2.8 Sociality plots